Radar target detection system for autonomous vehicles with ultra low phase noise frequency synthesizer

ABSTRACT

A system for detecting the surrounding environment of vehicle comprising a RADAR unit and at least one ultra-lowphase-noise frequency synthesizer, is provided. A RADAR unit configured for detecting the presence and characteristics of one or more objects in various directions. The RADAR unit may include a transmitter for transmitting at least one radio signal, and a receiver for receiving the at least one radio signalreturned from the one or more objects. The ultra-lowphase-noisefrequency synthesizer may utilize a dual loop design comprising one main PLL and one sampling PLL, where the main PLL might include a DDS or Fractional-N PLL plus a variable divider, or the synthesizer may utilize a sampling PLL only, to reduce phase-noise from the returned radio signal. This system enhances the detection of the exact location of the vehicle based on the received RADAR signatures of objects, azimuth and distance.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. application Ser. No.15/995,557 filed on Jun. 1, 2018.

FIELD

Embodiments of the present disclosure are generally related to sensorsfor autonomous vehicles (for example, Self-Driving Cars) and inparticular to systems that use ultra-low phase-noise frequencysynthesizer for RADAR Sensor Applications for autonomous vehicles.

BACKGROUND

Autonomous vehicles are paving way for a new mode of transportation.Autonomous vehicles require minimum or no intervention from vehicle'sdriver. Generally, some autonomous vehicles need only an initial inputfrom the driver, whereas some other designs of the autonomous vehiclesare continuously under control of the driver. There are some autonomousvehicles that can be remotely controlled. For example, automatic parkingin vehicles is an example of the autonomous vehicle in operation.

Autonomous vehicles face a dynamic environment that is the environmentkeeps changing every time. The autonomous vehicles need to keep a trackof lane markings, road edges, track road curves, varying surfaces thatmay include flat surfaces, winding roads, hilly roads etc. Alongside,the autonomous vehicles also need to keep a check on objects that areboth stationary or mobile like a tree or a human or an animal. Hence,the autonomous vehicles need to capture a huge amount of informationthat keeps on changing every time.

Therefore, to overcome and meet these challenges, autonomous vehiclesare provided with a various set of sensors. These sensors help thevehicle to gather all around information and help in increasing thedegree of autonomy of the vehicle. The various types of sensorscurrently being used in autonomous vehicles are LiDAR sensors,Ultrasonic sensors, Image sensors, Global Positioning System (GPS)sensors, Inertial Measurement Unit (IMU) sensors, dead reckoningsensors, Microbolo sensors, Speed sensors, Steering-angle sensors,Rotational speed sensors, Real-time Kinematics sensors, and RADARsensors. Two of the most used sensors are LiDAR and RADAR sensors.

LiDAR sensors: LiDAR is a device that maps objects in 3-dimensional bybouncing laser beams off its real-world surroundings. LiDAR inautomotive systems typically uses a 905 nm wavelength that can provideup to 200 m range in restricted FOVs (field of views). These sensorsscan the environment, around the vehicle, with a non-visible laser beam.LiDAR sensor continually fires off beams of laser light, and thenmeasures how long it takes for the light to return to the sensor. Thelaser beam generated is of low intensity and non-harmful. The beamvisualizes objects and measures ranges to create a 3D image of thevehicle's surrounding environment. LiDAR sensors are very accurate andcan gather information to even up to very close distances around thevehicle. However, LiDAR sensors are generally bulky, complex in designand expensive to use. The costs can be between around $8,000 and even upto $100,000. Smaller and less expensive LiDAR sensors are starting to bein the market. LiDAR may also require complex computing of the datacollected that also adds to the costs. Also, in general, LiDARs cancapture data up to a distance of around 200 m.

It is to be noted that LiDAR requires optical filters to removesensitivity to ambient light and to prevent spoofing from other LiDARs.Also, the laser technology used has to be “eye-safe”. Recently there areefforts being made to replace mechanical scanning LiDAR, that physicallyrotates the laser and receiver assembly to collect data over an areathat spans up to 360° with Solid State LiDAR (SSL). SSLs have no movingparts and are therefore more reliable especially in an automotiveenvironment that requires long-term reliability. However, SSLs currentlyhave lower field-of-view (FOV) coverage.

In current LiDAR sensor design coverage is also a problem in terms ofsensor gap and overlap, since the LiDAR in autonomous vehicles has verylimited redundancy sensors that can provide the level of imaging that aLiDAR provides under optimal conditions. LiDAR is also weathersusceptible. It turns blind when it comes to imaging in adverse weatherconditions. LiDAR has limitations of creating clear imaging inconditions of fog, rain, snow, direct sunlight, and darkness. Also,LiDAR cannot read letters on a signboard. This is so because thesignboard is flat.

RADAR sensors: RADAR sensors basically send out electromagnetic waves.When these waves hit an obstacle, they get reflected. Thus, revealinghow far away an object is and how fast is it moving.

RADAR sensors are very crucial in today's autonomous vehicleapplications. They are required to be more accurate.

Automotive RADARs can be categorized into three types: long-rangeRADARs, medium range RADARs, and short-range RADARs. Long range RADARsare used for measuring the distance to and speed of other vehicles.Medium range RADARs are used for detecting objects within a wider fieldof view e.g. for cross traffic alert systems. Short range RADARs areused for sensing in the vicinity of the car, e.g. for parking aid orobstacle detection. Depending on the application, RADAR requirementsdiffer. Short range applications require a steerable antenna with alarge scanning angle, creating a wide field of view. Long rangeapplications, on the other hand, require more directive antennas thatprovide a higher resolution within a more limited scanning range. Twodifferent frequency bands are mainly used for automotive RADARs: the 24GHz band and the 77 GHz band. The 77 GHz band offers higher performance,but it is also more challenging to implement since for example lossesare much higher at these frequencies. The 24 GHz RADARs are easier todevelop but are larger in size, making it difficult to integrate them ina vehicle. RADARs operating at 24 GHz require around three times largerantennas than RADARs operating at 77 GHz, to achieve the sameperformance. A 77 GHz RADAR would thus be much smaller resulting ineasier integration and lower cost. Moving to higher frequencies enablesRADARs with a better resolution. However, a major challenge posed is todevelop steerable antennas for 77 GHz RADARs with high enoughperformance at a reasonable cost. In one embodiment of the inventiondifferent types of antenna and meta-material-based antennas that areless prone to phase noise disturbance will benefit from this invention.

Some Automotive RADAR systems use a pulse-Doppler approach, where thetransmitter operates for a short period, known as the pulse repetitioninterval (PRI), then the system switches to receive mode until the nexttransmit pulse. As the RADAR signal returns, the reflections areprocessed coherently to extract range and relative motion of detectedobjects. Another approach is to use Continuous Wave Frequency Modulation(CWFM) or Frequency Modulated Continuous Wave (FMCW). This approach usesa continuous carrier frequency that varies over time with a receiverconstantly on. To prevent the transmit signal from leaking into thereceiver, separate transmit and receive antennas are used.

Generally, there are three types of RADARs in use in autonomousvehicles. Short range RADAR, that helps in collision warning, andprovide assisted parking support. Medium Range RADAR helps to watchcorners of the vehicle, help in blind spot detection, lane detection andavoid side/corner collisions. Further, long-range RADARs help inadaptive cruise control functions and early collision detectionfunctions.

RADAR signal processing also needs to be efficient. It needs tointelligently group the bouncing signals from the same object in therange. Otherwise, the RADAR signal processing will be overwhelmed withthe amount of signal processing and may get confused. Grouping is madepossible by use of Doppler shift of the signals bouncing off from thesurfaces with a. velocity different from the observation domain. Thus,Doppler maps are created, that depict the range to object returns on oneaxis and extracted velocity of the targets on the other.

RADARs have also been used for identifying and classifying humans.RADARs are not efficient in performing the human identification;however, there have been techniques that are being used for humanidentification.

One of the techniques uses reflectivity of humans using anultra-wideband RADAR. In this technique, the polarization of thereflected signal can be analyzed and can be determined that there aresome frequencies where, in one polarization, there is maximumreflectivity and a minimum in the other. However, the polarized signaldepends mainly on the shape, posture, and position of the person. Thus,making it a highly unreliable technique for classification.

Another technique used for human classification uses a dual-bandfrequency modulated continuous wave RADAR. In this technique, thedifference in reflected signal from an object at different frequencies(commonly 10 Hz to 66 Hz) is compared. Through this comparison, thethreshold for the ratio of the received intensity between twofrequencies, above which detected objects can be classified as animatedwas established.

Some techniques utilized and analyzed Doppler spectrum of CW RADAR toobtain a Doppler or micro-Doppler Signature for a walking human.Whereas, some of the techniques used wavelet transform to extract themicro signatures created by human walking. The same techniques can beutilized for other human movement and gesture control.

Other techniques that should be mentioned here include the use of 2 ormore different frequencies and multiple chirp types, These and thetechniques above support the evaluation and recognition of theelectromagnetic characteristics and properties of a human being oranother targeted object. These techniques can be used for materialdetection and human classification. Also, the usage of multiplefrequencies and chirp types provides information for micro-Doppler andradar signature evaluation and recognition.

The techniques mentioned are directed towards identification of a humanand distinguish them from other walking objects like animals. However,these techniques can also be used to recognize animals.

RADAR sensors are low priced and provide as excellent sensors. RADARsalso cost much less than LiDAR and may be procured within $150. Thesesensors work extremely accurately in bad weather conditions like fog,snow, dirt, etc. RADAR sensors use extremely simple circuitry and thusare smaller in size that makes them easy to be manufactured, installedand used. However, one of the major drawbacks of the RADAR sensors isthat they give confusing results when multiple objects are within therange. They are not able to filter noise in such situations. ExistingRADARs do not offer the necessary resolution to distinguish objects withsufficient reliability. One of the main problems faced is the separationof small and large objects that travel at the same distance and velocityin adjacent lanes, e.g. a motorcycle driving in the lane next to atruck.

Major factors affecting RADAR performance are described in the followingparagraphs:

Transmitter Power and Antenna:

The maximum range of a RADAR system depends in large part on the averagepower of its transmitter and the physical size of its antenna. This isalso called the power-aperture product. The antenna itself remains achallenge for autonomous vehicles and a lot is invested in variousantenna developments. The invention described here can be used with atype of antenna including antennas based and made out of meta-materials.In fact, the synergy between a meta-material antenna and this inventionwould result in a very high performing radar sensor.

Receiver Noise:

The sensitivity of a RADAR receiver is determined by the unavoidablenoise that appears at its input. At microwave RADAR frequencies, thenoise that limits detectability is usually generated by the receiveritself (i.e., by the random motion of electrons at the input of thereceiver) rather than by external noise that enters the receiver via theantenna.

Target Size:

The size of a target as “seen” by RADAR is not always related to thephysical size of the object. The measure of the target size as observedby RADAR is called RADAR cross-section and is determined in units ofarea (square meters). It is possible for two targets with the samephysical cross-sectional area to differ considerably in RADAR size orRADAR cross-section. For example, a flat plate 1 square meter in thearea will produce a RADAR cross-section of about 1,000 square meters ata frequency of 3 GHz when viewed perpendicular to the surface. Acone-sphere (an object resembling an ice-cream cone) when viewed in thedirection of the cone rather than the sphere could have a RADARcross-section of about 0.001 square meters even though its projectedarea is also 1 square meter. Hence, this may cause calculation mistakesand may give the wrong estimation of the objects identified.

Clutter:

Echoes from environmental factors like land, rain, birds and othersimilar objects may cause a nuisance to detect objects. Clutter makes itdifficult to identify objects and their properties to a considerableextent.

Interference:

Signals from nearby RADARs and other transmitters can be strong enoughto enter a RADAR receiver and produce spurious responses. Interferenceis not as easily ignored by automatic detection and tracking systems.Hence, interference may further add to noise to the RADAR signals.

Phase-Noise:

Phase-noise is defined as the noise created by short term phasefluctuations that occur in a signal. The fluctuations display themselvesin the frequency domain as sidebands which appear as a noise spectrumspreading out either side of the signal (Can be seen on FIG. 34)

Comparison Between LiDAR and RADAR

As compared to LiDAR sensors, RADAR sensors provide more robustinformation to the vehicles. LiDAR sensors are generally mounted on topof the vehicle and are mechanically rotated to gather surroundinginformation. This rotational movement is prone to dysfunction. Whereasin case of RADAR, as they are solid state and have no moving parts hencehave a minimal rate of failures.

Also, LiDAR sensors produce pulsed laser beams and hence are able togather information only when the pulsed beam is generating the laserbeams. RADAR sensors can generate continuous beams and hence providecontinuous information.

Also, LiDAR sensors generate enormous and complex data for which complexcomputational modules are required to be used. For example, some typesof LiDAR systems generate amounts of 1-Gb/s data that require asubstantial amount of computation by strong computers to process suchhigh amount of data in a timely manner. In some cases, these massivecomputations require additional computation and correlation ofinformation from other sensors and sources of information. Theseincreases cost overheads for vehicle manufacturers. Whereas, RADARsensors only generate small fractions of data that are easy to compute.

LiDAR sensors are also sensitive to adverse weather conditions such asrain, fog, and snow while RADAR sensors are not prone to weatherconditions. Though RADAR is not affected by darkness and it can workwell in adverse weather conditions, it may lower its resolution.

RADAR signatures of a walking human being are a big problem. Thesesignatures are not easily recognizable. Detection of human beings is aproblem to which most of the computing algorithms do not have goodsolutions. There are various algorithms known as segmenting algorithmsthat do provide a certain level of the solution. The processing engineof an autonomous vehicle may take input from RADAR, LiDAR, Camera,Ultrasound and other multiple sensors to build an image of thesurroundings of the vehicle.

Further, RADARs are generally used to detect receding and approachingobjects. Use of RADARs helps to decelerate the vehicle in applicablesituations and warn the driver.

Stationary RADAR sensors are also in use to monitor a predeterminedspace for e.g. railway crossings may be monitored using stationaryRADARs. The usage includes identification of objects in such railwaycrossings. In such situations either a warning can be generated, or thetrain may be decelerated. However, to effectively use the stationaryRADARs it is very important to be able to determine the size of theobjects identified by the RADAR sensor. These RADARs may include heightestimating systems for objects located in the range of such a RADAR.This needs to be accurate as even small amounts of deviations betweenthe plane and vertical sensor axis can result in large errors inestimating the object's size.

However, RADAR sensors are challenged when dealing with slow-movingobjects such as cars, bicycles, and pedestrians. Furthermore, thesetraditional RADAR systems, whether using a modulated or non-modulatedsignal, have difficulties identifying objects that are very close toeach other since one of them will be obscured due to the phase-noise ofthe system. Also, the drawback of existing RADAR sensors is the impacton their accuracy due to the phase-noise of its frequency source, thesynthesizer. RADAR sensors are not able to relay size and shape ofobjects as accurately as LiDAR. RADAR sensors might not be a stand-alonesolution. They can be accompanied by ultrasonic sensors or cameras.Though, RADAR is excellent in finding things that are solid over longdistances, but, it may be challenging to identify things that are in ashort range.

Therefore, there is a need for an enhanced detection system capable ofimplementing artificial intelligence using various sensory fusionincluding a plurality of LiDAR, Camera, Ultrasound, and RADAR sensorsfor helping in making informed decisions based on surroundinginformation for semi or autonomous vehicles. Furthermore, the systemshould be capable to overcome the shortcomings of the existing systemsand technologies.

SUMMARY

Some of the Benefits of the Invention:

The present invention emphasizes that by incorporating the ultra-lowphase-noise synthesizer in an existing RADAR system, the performance ofthe RADAR system will be improved substantially in terms of targetdetection accuracy and resolution and because of this it can become thedominant sensor for the handling of autonomous cars. Herein, theSynthesizer drastically reduces the phase-noise of RADAR signals so thatsuch RADAR sensor will be able to replace current sensor systems at verylow cost and with reliability at all lighting and adverse weatherconditions.

A system that utilizes an ultra-lowphase-noise synthesizer will be ableto provide data to a processor that can determine the electromagneticcharacteristics of an object with sufficient accuracy so that the systemis able to determine if the object is a living object such as a humanbeing or an animal or if it is inanimate. It will also be able toprovide data that is accurate enough to differentiate between thematerial objects are made of such as differentiating between wood andstone for example. As an example, the data generated by the RADAR systemcould be used to identify and verify the presence of a human on thesidewalk about to cross the street or a bicycle rider at the side of theroad.

Further as a derivative of the capability to determine the material anobject is made of combined with the electromagnetic waves capability topenetrate through many materials an object detection system utilizing anultra-lowphase-noise synthesizer will provide data that will enable aprocessing unit (such as a specialized processor of the object detectionsystem) to find objects that are visually obscured by another object anddetermine the material of the obscured and obscuring object. Thus, thesystem may be able to find a human behind a billboard/bus stationadvertisement or wildlife behind a bush or determine that these are only2 bushes (or non-animated objects) one behind the other.

Further, a Radar system that utilizes an ultra-low phase-noisesynthesizer will also include the possibility to be implemented inside avehicles cabin. In such an implementation the Radar system would be ableto count passengers inside the vehicle and monitor their vital signs andeven detect the eyelid movement of the driver to detect fatigue forinstance. In addition, such a radar system could be used in anautonomous vehicle used for ride sharing to determine if anything hasbeen forgotten inside the vehicle or if any type of dirt or litter hasbeen left behind or if the vehicle has been soiled.

In addition, a Radar system that utilizes an ultra-low phase-noisesynthesizer will benefit from improved capabilities such asidentification of small movements of an object through improvedmicro-doppler performance for example. That way such a radar systemcould identify small movements of limbs and gestures and other smalldetails of the environment. This feature could, for example, be used inan embodiment to improve the prediction of human behavior.

Additionally, a RADAR system that utilizes an ultra-lowphase-noisesynthesizer may be used as an imaging RADAR that can discoversilhouettes and create a true 3-dimensional map of the surroundings ofthe vehicle including the mapping of the objects that are not visiblewith light. Such a RADAR System would also be able to utilize SyntheticAperture Radar (SAR) technology, Interferometry and Polarimetry (orother SAR related technologies) to define the exact characteristics ofan objects backscatter such as, but not limited to, Surface roughness,Geometric structure, Orientation and more. Further, anultra-lowphase-noise RADAR system enables the determination ofelectrical characteristic such as, but not limited to, Dielectricconstant, Moisture content, Conductivity and more. The data creation ofelectromagnetic characteristics can also be achieved by combining theultra-lowphase-noise synthesizer of this invention and using 2 or moredifferent frequencies and multiple chirp types.

According to an embodiment of the present disclosure an object detectionsystem for autonomous vehicles is provided, The object detection systemmay include a RADAR unit coupled to at least one ultra-lowphase-noisefrequency synthesizer, configured for detecting the presence of one ormore objects in one or more directions, the RADAR unit comprising: atransmitter for transmitting at least one radio signal; and a receiverfor receiving at least one radio signalreturned from one or moreobjects/targets. Further, the object detection system may include the atleast one ultra-low phase-noise frequency synthesizer that may beutilized in conjunction with the RADAR unit, for refining both thetransmitted and the received signals, and thus determining thephase-noise and maintaining the quality of the transmitted and thereceived radio signals, wherein the at least one ultra-low phase-noisefrequency synthesizer comprises: (i) at least one clocking deviceconfigured to generate at least one first clock signal of at least onefirst clock frequency; (ii) at least one sampling Phase Locked Loop(PLL), wherein the at least one sampling PLL comprises: (a) at least onesampling phase detector configured to receive the at least one firstclock signal and a single reference frequency to generate at least onefirst analog control voltage; and (b) at least one reference VoltageControlled Oscillator (VCO) configured to receive the at least oneanalog control voltage to generate the single reference frequency; and(c) a Digital Phase/Frequency detector configured to receive the atleast one first clock signal and a single reference frequency togenerate at least a second analog control voltage; and (d) a two-way DCswitch in communication with the Digital Phase/Frequency detector andthe sampling phase detector; (iii) at least one first fixed frequencydivider configured to receive the at least one reference frequency andto divide the at least one reference frequency by a first predefinedfactor to generate at least one clock signal for at least one highfrequency low phase-noise Direct Digital Synthesizer (DDS) clock signal;(iv) at least one high-frequency low phase-noise DDS configured toreceive the at least one DDS clock signal and to generate at least onesecond clock signal of at least one second clock frequency; and (v) atleast one main Phase Locked Loop (PLL).

Hereinabove, the main PLL may include: (a) at least one high-frequencyDigital Phase/Frequency detector configured to receive and compare theat least one second clock frequency and at least one feedback frequencyto generate at least one second analog control voltage and at least onedigital control voltage; (b) at least one main VCO configured to receivethe at least one first analog control voltage or the at least one secondanalog control voltage and generate at least one output signal of atleast one output frequency, wherein the at least one digital controlvoltage controls which of the at least one first analog control voltageor the at least one second analog control voltage is received by the atleast one main VCO; (c) at least one down convert mixer configured tomix the at least one output frequency and the reference frequency togenerate at least one intermediate frequency; and (d) at least onesecond fixed frequency divider configured to receive and divide the atleast one intermediate frequency by a second predefined factor togenerate the at least one feedback frequency.

Herein, the RADAR unit or units create a 3-dimensional RADAR image usingone or more RADAR sensors and/or one or more frequencies. Thetransmitting RADAR may be at one location of the vehicle while thereceiving unit is at another location. The RADAR sensors may utilizeSynthetic aperture RADAR (SAR) technology to create the 3-dimensionalimage. The 3-dimensional image may include information about objectsthat are obscured by visible light. In an embodiment, Bi-static andmulti-static may also involve one vehicle transmitting while one or moreother vehicles receive the return signals.

Further, the data from the Radar unit comprising an ultra-lowphase-noisesynthesizer can and should be used for improved compressed sensing,micro-Doppler classification, object classification by electromagneticcharacteristics and radar-based mapping of cities, roads and othervenues that can or are being mapped with visual sensors

Also, in an embodiment of the invention presented here, the system canuse a type of antenna including antennas made out of meta-materials orother materials.

Herein, the radar unit determines a distance and a direction of each ofone or more objects. Further, the radar unit determines one or morecharacteristics, of two close objects irrespective of the size of theone or more objects. Again further, the radar unit differentiatesbetween two or more types of the objects when one object is visuallyobscuring another object. Additionally, the radar unit utilizes amodulated or non-modulated radio signal, to determine the presence of aslow-moving target despite the very small Doppler frequency shift. Also,the radar unit utilizes a modulated or non-modulated radio signal, todetermine the presence of a close-range target despite the very shortsignal travel time.

Additionally, a vehicle with RADAR imaging capabilities may createcontact with other vehicles that have that same feature. The group of 2vehicles or more will have an identification scheme, or will set up one,so that every vehicle will be able to detect return signals from everyother vehicle and thus combine a 3-dimensional map of the surroundingsfor immediate autonomous driving purposes and/or mapping or anotherpurpose. This modus operandi should not be limited only to vehiclestransmitting and receiving a Radar signal, the transmitter or receivercan also be stationary, such as a radar on a traffic light that warnsvehicles about congestion at a crossing. Actions derived from thatinformation might include slowing down or having the GPS recalculate theroute to avoid congestion.

The object detection system may further include at least one additionalsensor system, available on the autonomous vehicle, or a databaseconnection in conjunction with the RADAR unit. The combined objectdetection and/or classification and imaging system may be used asreal-time sensors and/or as (real-time) mapping device for single ormultiple vehicles use through a direct vehicle-to-vehicle (V2V) or otherconnectivity solution

Further, considering the example of the pedestrian on the sidewalk orthe bicycle rider at the side of the road once the RADAR unit hasdetected something of interest this can be used in conjunction withother sensors, a LiDAR device for instance. In such a case the visiblefield for the LiDAR could be reduced to 1/100 of its usual Field of View(FOV) and the elevation angle by another 1/10 to 1/100 of its originalFOV reducing the computation needed for the LiDAR by 1/1000 to 1/10000.

Further, the at least one ultra-low phase-noise frequency synthesizerfurther comprises at least one fixed frequency multiplier configured toreceive and multiply the at least one output signal generated by the atleast one main PLL by a predefined factor to generate at least one finaloutput signal of at least one final output frequency. The at least oneultra-low phase-noise frequency synthesizer is implemented on the sameelectronic circuitry or on a separate electronic circuitry. Further, theultra-low phase-noise frequency synthesizer may be used to generate theup or down converting the signal of the RADAR unit.

Further, according to another embodiment of the present disclosure, amethod for autonomous vehicles is disclosed. The method may include (butis not limited to): detecting a presence of one or more objects in oneor more directions by a RADAR unit. Herein, the RADAR unit comprising: atransmitter for transmitting at least one radio signal to the one ormore objects; and a receiver for receiving the at least one radio signalreturned from the one or more objects. Further, the method may includeperforming, by at least one ultra-low phase-noise frequency synthesizerfor refining the transmitted signal and not adding phase-noise to thereceived signals, and thereby determining a phase-noise and maintainingthe quality of the transmitted and the received radio signals.

Herein, the method may further include various steps such as receivingand multiplying, by the ultra-low phase-noise frequency synthesizer, theat least one output signal by a predefined factor to generate at leastone final output signal of at least one final output frequency. Further,the method may generate the up converting or down converting the signalof the RADAR unit. Furthermore, the method may determine the presence ofa slow-moving target despite the very small Doppler frequency shift.Again further, the method may include determining the presence of aclose-range target despite the very short signal travel time.Additionally, the method may determine a distance and a direction ofeach of the one or more objects. Furthermore, the method may determine atype of material an object is made up of. Also, the method may include astep of activating one or more additional sensors for operation thereofin conjunction with the RADAR unit. The method may determinecharacteristics of two close objects irrespective of the size of theobjects. Further, the method may differentiate between two or more typesof the objects when one object is visually obscuring another object.Further, the method may improve techniques such as compressed sensing,micro-Doppler classification and object classification according to itselectromagnetic characteristics.

According to an embodiment of the present disclosure, a system is adetection system that comprises a RADAR unit, communicably coupled to atleast one ultra-low phase-noise frequency synthesizer, is provided. TheRADAR unit configured for detecting the presence of one or more objectsin one or more directions. Herein, the RADAR unit comprising: atransmitter for transmitting at least one radio signal; and a receiverfor receiving at least one radio signal returned from one or moreobjects/targets. Further, the detection system may include at least oneultra-low phase-noise frequency synthesizer that may be configured forrefining the returning the at least one radio signal to reducephase-noise therefrom.

Herein, the system includes at least one ultra-low phase-noise frequencysynthesizer configured to determine phase-noise and quality of thetransmitted and the received at least one radio signal. Theultra-lowphase-noise frequency synthesizer is a critical part of aSystem, regardless of how it is implemented. The ultra-lowphase-noisefrequency synthesizer comprises one main PLL (Phase Lock Loop) and onereference sampling PLL. The main PLL comprises one high-frequency DDS(Direct Digital Synthesizer), one Digital Phase Frequency Detector, onemain VCO (Voltage Controlled Oscillator), one internal frequencydivider, one output frequency divider or multiplier and one down convertmixer. The reference sampling PLL comprises one reference clock, onesampling phase detector, one digital phase/frequency detector and onereference VCO. This embodiment provides a vast and critical improvementin the overall system output phase-noise. The synthesizer design isbased on the following technical approaches—a) using of dual loopapproach to reducing frequency multiplication number, b) using ofsampling PLL as the reference PLL to make its noise contributionnegligible, c) using of DDS to provide high-frequency input to the mainPLL and d) using of high-frequency Digital Phase Frequency Detector inthe main PLL.

In an additional embodiment of present disclosure, the system includesat least one ultra-low phase-noise frequency synthesizer configured todetermine phase-noise and quality of the transmitted and the received atleast one radio signal. The ultra-low phase-noise frequency synthesizercomprises one main PLL (Phase Lock Loop) and one reference sampling PLL.The main PLL further comprises one Fractional-N Synthesizer chip, oneprimary VCO (Voltage Controlled Oscillator) and one down convert mixer.The Fractional-N Synthesizer chip includes one Digital Phase Detectorand one software controllable variable frequency divider. The referencesampling PLL comprises one reference clock, one sampling phase detector,one digital phase/frequency detector and one reference VCO. Thisembodiment provides multiple improvements in system output which arebased on the following technical approaches—a) using of dual loopapproach to reducing frequency multiplication number, b) using ofsampling PLL to make its noise contribution negligible, and c) using ofa high-frequency Fractional-N Synthesizer chip in the main PLL.

In an additional embodiment of present disclosure, the system includesat least one ultra-low phase-noise frequency synthesizer configured todetermine phase-noise and quality of the transmitted and the received atleast one radio signal. The ultra-lowphase-noise frequency synthesizercomprises one sampling PLL. The sampling PLL comprises one referenceclock, one sampling phase detector, one digital phase/frequency detectorand one VCO.

According to an embodiment of the present disclosure, a detection systemcomprising a RADAR unit and an ultra-lowphase-noise frequencysynthesizer is provided. The system is made up of System on Chip (SoC)module. The RADAR unit configured for detecting the presence or imagingof one or more objects in one or more directions. The RADAR unitcomprising: a transmitter for transmitting at least one radio signal;and a receiver for receiving the at least one radio signalreturned fromthe one or more objects/targets. In an embodiment, the Transmit andreceive signal frequencies might be equal. For example, if there is noDoppler effect, the signal frequencies may be equal. In an embodiment,the transmit and receive frequencies might also be different, forexample in cases where the Doppler effect is present. Theultra-lowphase-noise frequency synthesizer comprises one main PLL (PhaseLock Loop) and one reference sampling PLL. The main PLL furthercomprises one Fractional-N Synthesizer chip, one primary VCO (VoltageControlled Oscillator) and one down convert mixer. The Fractional-NSynthesizer chip includes one Digital Phase Detector and one softwarecontrollable variable frequency divider. The reference sampling PLLcomprises one sampling PLL, and one reference VCO. This embodimentprovides multiple improvements in system output which are based on thefollowing technical approaches—a) using of dual loop approach toreducing frequency multiplication number, b) using of sampling PLL tomake its noise contribution negligible, and c) using of a high-frequencyFractional-N Synthesizer chip in the main PLL.

In an additional embodiment of the present disclosure, a vehicle havinga detection system is disclosed. The detection system may be implementedfor detecting information corresponding to one or more objects, thedetection unit comprising: a RADAR unit for transmitting radio signalsand further for receiving the returned radio signal(s) from one or moreobjects/targets; and at least one ultra-low phase-noise frequencysynthesizer for refining the returned signals to reduce the effect ofphase-noise in the returned radio signals. Further, the detection unitcomprises a processor for processing the refined signals to determineone or more characteristics corresponding to the one or more objects,the processor determining one or more actions based on one or morefactors and the one or more characteristics corresponding to the one ormore objects. The processor further may determine one or more actionsbeing adaptable by the vehicle based on one or more characteristics thatmay originate from the RADAR system and/or in conjunction withinformation originated from another sensor. The vehicle further includesone or more components communicably coupled to the processor forperforming the determined one or more actions.

The detection system may further include a memory for storinginformation and characteristics corresponding to the one or moreobjects, and actions performed by the vehicle.

Hereinabove, the at least one ultra-low phase-noise frequencysynthesizer may be implemented in a manner as described further in thedetailed description of this disclosure. Further, the RADAR unitcomprises at least one of: traditional single antenna RADAR, dual ormulti-antenna RADAR, synthetic aperture RADAR, and one or more otherRADARs. Further, in an embodiment, the processor may determine phaseshift in frequencies of the transmitted radio signals and the returnedradio signals. Such phase shift (difference in phase-noise frequency)may further be analyzed in light of a frequency of the refined radiosignal to self-evaluate overall performance of the detection system (orspecific performance of the ultra-low phase-noise frequencysynthesizer).

The preceding is a simplified summary to provide an understanding ofsome aspects of embodiments of the present disclosure. This summary isneither an extensive nor exhaustive overview of the present disclosureand its various embodiments. The summary presents selected concepts ofthe embodiments of the present disclosure in a simplified form as anintroduction to the more detailed description presented below. As willbe appreciated, other embodiments of the present disclosure are possibleutilizing, alone or in combination, one or more of the features setforth above or described in detail below.

Current Radar sensors that reside in existing vehicles provide coarseinformation about the vehicles surroundings. This invention discloses asystem that utilizes a new and innovative frequency generation mechanism(Synthesizer) to improve the Radar sensor performance significantly. Thespectral purity (Phase Noise) of a local oscillator (LO) of a radarsystem is usually perceived as a parameter that cannot be manipulated orimproved. In addition, many radar systems in use today implement asingle LO that serves the transmit and receive paths so that thephase-noise close to the carrier frequency is assumed to be cancelled byself-correlation. This assumption originates because near field echoes,that have a very short travel time, will find the LO approximately inthe same state as it was during transmission. As a result, thephase-noise is considered partially cancelled out for low frequencydeltas around the LO.

Doppler Shift:

Single LO Radar systems are common in Assisted driving systems andautonomous vehicles which turns the emphasis on the statement above tothe phrase “the phase-noise is partially cancelled”. The fact that thephase-noise partially cancelled while the signal processing of the Radarsystems practically assumes that the phase-noise is completely canceledcreates a few disadvantages since these Radars rely heavily on thedoppler principle, i.e.: based on frequency shifts. When utilizing theDoppler principal every stationary object theoretically creates areflected signal that lands exactly on DC after down-conversion. In thepresence of phase-noise, stationary objects have some velocity around DCdue to that phase-noise. In this specification we will call that“Doppler Jitter” which is generally caused by clutter. In order toreduce the effect of clutter the DC portion of the signal is disregardedfor signal processing purposes.

This in turn causes one of 2 issues:

-   -   1. 1st Option—Some clutter still remains because of the doppler        jitter, and then slow-moving objects such as walking human        beings are obscured by the clutter    -   2. 2nd Option—The Signal processing removes some bandwidth        around DC, and then slow-moving objects are completely        disregarded.

Both of the options above bare disadvantages which are furtherexacerbated as follows:

-   -   1. The Radar Cross Section (RCS) of a human is significantly        smaller of a car. In some cases, a human's radar cross section        is about 10 to 100 times smaller that of a car or approximately        in the order 0 dBsm. On the contrary the RCS of a small car can        be between 10 to 20 dBsm, depending on the side the Radar hits        the car.    -   2. The slower the vehicle that carries the radar sensor moves        the smaller the doppler shift for slow moving objects—a scenario        that is common in urban areas where cars and humans coexist.    -   3. In addition to 2 above, the urban environment is also        challenging because the distance between vehicle and other        surrounding objects may be very short so that the return signal        will suffer from one of the 2 issues mentioned above.    -   4. Micro Doppler—the movement of limbs creates very small        frequency shifts with very small return signal amplitudes and        often times in the opposite or different direction of the        generic movement of the human, this again brings us back to the        2 unwanted options in the section above.

To summarize the disadvantages mentioned above with respect to theDoppler frequency shift and phase noise: it is very hard to detectobjects that move slow and have a small Radar Cross Section (RCS), suchas pedestrians or bicyclists.

On the contrary, with ultra-low phase-noise, it is much simpler toidentify and classify objects that move slow and have a small RadarCross Section (RCS), such as pedestrians or bicyclists. Further it iseasier to compute the processing their velocity and location even if thedistance is small. Further, Ultra-Low Phase-Noise opens an entire domainof micro-Doppler signal processing.

Phase Noise Amplification:

Radar System uses amplifiers in the transmit, receive and LOdistribution paths. All of these amplifiers suffer from a non-lineardeficiency usually referred to as “1/f Noise”. Meaning that theamplifier adds more noise closer to the signal it amplifies that furtheraway from it. Vehicle mounted Radar systems usually use a form ofFrequency Modulated Continuous Wave (FMCW) which originate from the LOand already carry noise with them in the form of Phase Noise. When thesesignals travel through an amplifier, the phase-noise is essentiallyamplified according to 1/f distance from the CW frequency. Whenimplementing the disclosed radar with ultra-low phase-noise synthesizerthis kind of spectral contamination is reduced by 100-1000 times.

Pulse Compression

The following section discusses pulse compression, which is a signalprocessing method very often utilized together with FMCW. During thepulse compression signal creation, the phase-noise of the signal showsas sidelobes or elevated noise around the processed radar echo whichessentially triggers 2 ripple effects:

-   -   1. Reduced Signal to Noise Ratio diminishes the capability of        object classification (especially objects with a small RCS)    -   2. Reduced Signal to Noise Ratio causes worse Radar sensitivity        and results in less range        Additional Advantages of Ultra-Low Phase-Noise

Many Radar systems implement beam forming mechanisms. Whether thesemechanisms rely on phase shifting or electronic beamforming, thephase-noise in the system will always add an error in the actualdirection of the beam, therefore it is intuitively understandable thatwith lower phase-noise better angular accuracy of the beam can beachieved.

Although not very advanced in the automotive world, another item thatshould be mentioned is Synthetic Aperture Radar (SAR) imaging. SARimaging is in use by aircrafts and satellites for many years and theimportance of phase-noise is well understood for the different methodsof SAR imaging.

Furthermore, different objects may be consistent or are made out ofdifferent materials that have different electromagnetic characteristics,and therefore they will have a return signal that are not only specificto the object's velocity, size and location, but also specific to itselectromagnetic characteristics.

Sensory Fusion:

Ultra-low phase-noise based radar may experience and “see” theenvironment in different way than camera, LiDAR, ultrasonic sensor orother sensors. In this way the Ultra-low phase-noise based radar “see”non-visual information that is not visible to other sensors. Thedetection range of such radar can be much larger than other sensors andspecifically with adverse weather and lighting. Different objects returndifferent radar signatures that can be used for classification anddetection of such object using Artificial Intelligence (AI) and MachineLearning (ML).

In an embodiment of this invention the ultra-low phase noise Radar maystart the detection, classification and perception process at a greaterrange than traditional Radars, thus it may provide an early warning forother sensors and ample time for a processing unit that may process thedata, poll data from sensors, run AI/ML algorithms and perform decisionmaking processes.

Ample time of advanced notice about the potential and probability of anobject can be fused with other sensors for early processing and focusingto the area and direction of the suspected object. Such objectclassification may include the statistical probability and likelihoodfor classification of the detected object in a range. For example,classification probability of P₁% detection in range R₁, P₂% in rangeR₂, and P₃% in range R₃. In case of detection and classification at asubstantially larger range by the radar, the data can be fused andcomputed as an input or trigger for different sensors.

Some Benefits:

The implementation of ultra-low phase-noise synthesizers in Radarsystems has been discussed in detail for the Doppler phenomenon, 1/fnoise and pulse compression. Further, beamforming, SAR imaging andmaterial recognition have been mentioned at a high level. Followingprimary and secondary conclusions can be derived:

-   -   1. Primary advantages for the implementation of ultra-low        phase-noise synthesizers in Radar systems are:        -   a. Much better accuracy in the Doppler and micro-Doppler            domains        -   b. Signal processing improvements that have an impact on the            accuracy and the range of the Radar sensor        -   c. Improved SAR imaging and more accurate usage of            beamforming mechanisms    -   2. Secondary advantages for the implementation of ultra-low        phase-noise synthesizers in Radar systems are:        -   a. The sensory fusion mechanisms will have more accurate            Radar sensor data at their disposal and new information            vectors that were previously non-existing.        -   b. Since Radar is not a “visual sensor” per se, true sensory            fusion is made possible.        -   c. A single radar sensor can create multiple information            vectors about a single object. This satisfies the most            important objective of identifying living objects such as a            human or an animal.

In one embodiment of the present invention, a system for detecting thesurrounding environment of a vehicle is disclosed. The system mayinclude, but is not limited to, at least first sensor and a processingunit. The at least first sensor may be configured to obtain data. The atleast first sensor may include (but is not limited to) a transmitter fortransmitting at least one radio signal to the one or more objects withinthe surrounding environment. Further, the at least one sensor mayinclude a receiver for receiving the at least one radio signal returnedfrom the one or more objects. Furthermore, the at least one sensor mayinclude at least one ultra-low phase noise frequency synthesizerconfigured to determine phase noise and quality of the transmitted andthe received at least one radio signal, wherein the at least oneultra-low phase noise frequency synthesizer comprises at least onesampling Phase Locked Loop (PLL) and at least one main PLL, saidSampling PLL comprises a sampling phase detector and said main PLLcomprises a high frequency digital phase/frequency detector. Further,the processing unit coupled to the at least first sensor configured to:gather, electro-magnetic information about the one or more objects;classify or recognize each of the one or more objects by analyzing thedata, wherein the classification or recognition is based on a uniquesignature obtained from each of the one or more objects; generate anelectromagnetic map of the surrounding environment by utilizing uniquesignatures of the one or more objects; and combine the electromagneticmap with a geographical map or physical map.

Hereinabove, the at least first sensor is a RADAR sensor. Further, thedata includes information about the electromagnetic properties andcharacteristics about the object of interest or the surroundings of thevehicle. The data may include (but is not limited to) shape, silhouette,doppler or micro doppler information. Further, the data includes depth,dimensions, direction, height, distance and placement of the object ofinterest with respect to the vehicle. Furthermore, the classificationtype of the one or more objects includes living or non-living thing,stationary or moving object, animal or human, standing or mobile human,metal, wood, or concrete.

In another embodiment of the invention, a method for detecting thesurrounding environment of a vehicle is disclosed. The method mayinclude (but not limited to) utilizing at least a first sensor to obtaina data. The at least first sensor being utilized for: transmitting, by atransmitter, at least one radio signal to the one or more objects withinthe surrounding environment; receiving, by a receiver, the at least oneradio signal returned from the one or more objects; and determiningphase noise and quality of the transmitted and the received at least oneradio signal by at least one ultra-low phase noise frequencysynthesizer, wherein the at least one ultra-low phase noise frequencysynthesizer comprises at least one sampling Phase Locked Loop (PLL) andat least one main PLL, said Sampling PLL comprises a sampling phasedetector and said main PLL comprises a high frequency digitalphase/frequency detector. The method may further include gathering,electro-magnetic information about the one or more objects; classifyingor recognize each of the one or more objects by analyzing the data,wherein the classification or recognition is based on a unique signatureobtained from each of the one or more objects; generating anelectromagnetic map of the surrounding environment by utilizing uniquesignatures of the one or more objects; and combining the electromagneticmap with a geographical map or physical map.

Hereinabove, the method may further include recognizing the object oninterest after classifying, wherein the classification of the object ofinterest includes living or non-living thing, stationary or movingobject, animal or human, standing or mobile human, metallic, wooden, orconcrete objects.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and still further features and advantages of embodiments ofthe present invention will become apparent upon consideration of thefollowing detailed description of embodiments thereof, especially whentaken in conjunction with the accompanying drawings, and wherein:

FIG. 1 illustrates a general block diagram of a negative feedbacksystem;

FIG. 2 illustrates a general block diagram of a standard Phase Lock Loop(PLL);

FIG. 3 illustrates a simplified drawing of a digital phase/frequencydetector;

FIG. 4 illustrates an example of an active filter as applied to ageneral PLL;

FIG. 5 illustrates the principle of the sample-and-hold mechanism;

FIG. 6 illustrates a schematic of the step recovery diode as combgenerator feeding the dual Schottky diode that acts as phase detector;

FIG. 7 illustrates a complete example schematic of the comb generatorand sampling phase detector with RF pre-amplifier and two DC buffersfollowing the phase detector;

FIG. 8 illustrates a phase-noise plot of an example free running VoltageControl Oscillator (VCO) in the frequency domain (spectrum analyzer),without being locked in a PLL;

FIG. 9 illustrates a phase-noise plot of an example Voltage ControlOscillator (VCO) in the frequency domain (spectrum analyzer),compensated by being locked in a PLL;

FIG. 10 illustrates two plots: (a) a simulation of phase-noise of anexample PLL, and (b) is an actual measurement;

FIG. 11 illustrates a phase-noise plot of a closed loop PLL, showingclearly the effect of the phase detector multiplication number 20*LOG(N)within loop bandwidth;

FIG. 12 illustrates a plot of measurement in terms of phase-noise in 1Hz bandwidth at aΔf offset frequency from the carrier;

FIG. 13 illustrates a general block diagram of an example dual loop PLL;

FIG. 14 illustrates a general block diagram of an example dual samplingPLL;

FIG. 15 illustrates how impulse or “comb” generator changes a wave shapeof a signal from a sine wave to pulses;

FIG. 16 illustrates an example output of a comb generator in thefrequency domain;

FIG. 17 illustrates a block diagram of an ultra-low phase-noisefrequency synthesizer as suggested in a first embodiment;

FIG. 18 illustrates a block diagram of an ultra-low phase-noisefrequency synthesizer as suggested in a second embodiment;

FIG. 19 illustrates a block diagram of the sampling PLL system;

FIG. 20 illustrates a phase-noise simulation plot contributed by a DDSchip in accordance with the first embodiment of the present disclosure;

FIG. 21 illustrates a phase-noise simulation plot contributed by themain PLL in accordance with the first embodiment of the presentdisclosure;

FIG. 22 illustrates a phase-noise simulation plot contributed by areference sampling PLL having the TCXO clock (or other reference Clock)generating input frequencies of 100 MHz in accordance with the firstembodiment of the present disclosure;

FIG. 23 illustrates a phase-noise simulation plot contributed by areference sampling PLL having the TCXO clock (or other reference Clock)generating input frequencies of 250 MHz in accordance with the firstembodiment of the present disclosure;

FIG. 24 illustrates a phase-noise simulation plot contributed by themain PLL in accordance with the second embodiment of the presentdisclosure;

FIG. 25 illustrates a phase-noise simulation plot contributed by areference sampling PLL having the TCXO clock (or other reference Clock)generating input frequencies of 100 MHz in accordance with the secondembodiment of the present disclosure;

FIG. 26 illustrates a phase-noise simulation plot contributed by areference sampling PLL having the TCXO clock (or other reference Clock)generating input frequencies of 250 MHz in accordance with the secondembodiment of the present disclosure;

FIG. 27 illustrates a flowchart depicting the operational method stepsof the first embodiment;

FIG. 28 illustrates a flowchart depicting the operational method stepsof the second embodiment;

FIG. 29 illustrates a flowchart depicting the operational method stepsof the sampling PLL;

FIGS. 30A, 30B, 31-36 correspond to prior arts and existingtechnologies;

FIG. 37 illustrates a detection system, in accordance with variousembodiments of the present invention;

FIG. 38 illustrates an exemplary vehicle implementing detection system,in accordance with an embodiment of the present invention;

FIG. 39 illustrates a block diagram of an exemplary RADAR, in accordancewith an embodiment of the invention;

FIG. 40A illustrates an exemplary 3-dimensional map generation using theRADAR, in accordance with an embodiment of the invention;

FIG. 40B illustrates another exemplary 3-dimensional map generationusing the RADAR, in accordance with an embodiment of the invention;

FIG. 41 illustrates another example of 3-dimensional mapping using theRADAR, in accordance with an embodiment of the invention;

FIG. 42 illustrates a flowchart depicting an overall method, inaccordance with an embodiment of the invention;

FIG. 43 illustrates a flowchart depicting the operational method stepsof the first embodiment, in accordance with another embodiment of theinvention;

FIG. 44 illustrates a line diagram depicting an improvement in RADARsignals, in accordance with the present invention;

FIG. 45 illustrates a line diagram depicting object identification, inaccordance with an embodiment of the present invention;

FIG. 46(FIG. 46A and FIG. 46B) illustrates a line diagram depictingidentification of obscured objects, in accordance with an embodiment ofthe present invention;

FIG. 47 illustrates a line diagram depicting identification of road andpavement, in accordance with an embodiment of the present invention;

FIG. 48 illustrates a block diagram of a processor, in accordance withan embodiment of the present invention;

FIG. 49 illustrates a flowchart of a method for identifying live objectsusing the detection system, in accordance with an embodiment of theinvention;

FIG. 50 illustrates a block diagram of a detection system, in accordancewith an embodiment of the invention;

FIG. 51 illustrates a method for detecting and imaging objects for avehicle, in accordance with an embodiment of the invention;

FIG. 52 illustrates a block diagram of a processor and its variousinternal components to perform detection of objects, in accordance withan embodiment of the invention;

FIG. 53 illustrates a flowchart for a method of identification of apotential threat object, in accordance with an embodiment of theinvention;

FIG. 54, illustrates a block diagram displaying advantages of usingselective use of the at least second sensor, in accordance with anembodiment of the invention;

FIG. 55, illustrates a block diagram displaying advantages of usingselective use of the at least second sensor, in accordance with anembodiment of the invention;

FIG. 56 illustrates a flowchart for a method for recognition of a livingbody, in accordance with an embodiment of the invention;

FIG. 57 illustrates an exemplary method flow diagram for detecting andimaging objects for a vehicle, in accordance with an embodiment of theinvention;

FIG. 58 illustrates a block diagram of a detection system for detectingsurrounding environment, in accordance with an embodiment of theinvention;

FIG. 59 illustrates a block diagram of the processor and its variousinternal components to perform detection of objects, according to anembodiment of the invention;

FIG. 60 illustrates a block diagram of system depicting formation ofRADAR maps, in accordance with an embodiment of the present invention;

FIG. 61 illustrates a block diagram of a detection system for RADAR mapsgeneration, in accordance with an embodiment of the invention;

FIG. 62 illustrates a block diagram of the processor and its variousinternal components to perform detection of objects, according to anembodiment of the invention;

FIG. 63 illustrates a flow chart illustrating a method for RADAR mapgeneration, in accordance with an embodiment of the invention;

FIG. 64 illustrates an exemplary embodiment of a collective mappingsystem to build a collective map of the surrounding environment, inaccordance with an embodiment of the invention;

FIG. 65A illustrates a block diagram of the processor and its internalprocessing modules, according to one embodiment of the invention;

FIG. 65B illustrates a block diagram of the processor and its internalprocessing modules, according to another embodiment of the invention;

FIG. 66 illustrates a flow chart representing a method performed by thesystem for creating collective map data, in accordance with anembodiment of the invention;

FIG. 67A illustrates a block diagram of a surrounding environment alongwith various objects within the surrounding environment, in accordancewith an embodiment of the invention;

FIG. 67B illustrates localization of the vehicle while traversing theenvironment, in accordance with an embodiment of the invention;

FIG. 68 illustrates a flow chart depicting a method for generation ofnavigational maps, in accordance with an embodiment of the invention;

FIG. 69, illustrates a Radar upchirp and downchirp without phase-noiseand the effect on the beat frequency;

FIG. 70, illustrates a Radar upchirp and downchirp with phase-noise andthe effect on the beat frequency;

FIG. 71, illustrates upchirps and downchirps without phase-noise whentwo “real objects” are present and the “ghost objects” created by thesystem;

FIG. 72, illustrates upchirps and downchirps, with phase when two “realobjects” are present and the “ghost objects” that are created by thesystem and their data blurred by phase-noise;

FIG. 73, illustrates the Fourier transform of the beat frequency withoutthe presence of phase-noise;

FIG. 74, illustrates the Fourier transform of the beat frequency withthe presence of phase-noise;

FIG. 75, illustrates the method of detecting a stationary object usingthe properties of phase-noise;

FIG. 76 illustrates a common analysis of Radar signals on 2 dimensionalFFT grid, in accordance with an embodiment of the invention; and

FIG. 77 illustrates an exemplary embodiment of a long range, ample timeadvanced notice detection and classification system, in accordance withan embodiment of the invention.

To facilitate understanding, like reference numerals have been used,where possible, to designate like elements common to the figures.

DETAILED DESCRIPTION

As used throughout this application, the word “may” be used in apermissive sense (i.e., meaning having the potential to), rather thanthe mandatory sense (i.e., meaning must). Similarly, the words“include”, “including”, and “includes” mean including but not limitedto.

The phrases “at least one”, “one or more”, and “and/or” are open-endedexpressions that are both conjunctive and disjunctive in operation. Forexample, each of the expressions “at least one of A, B and C”, “at leastone of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B,or C” and “A, B, and/or C” means A alone, B alone, C alone, A and Btogether, A and C together, B and C together, or A, B and C together.

The term “a” or “an” entity refers to one or more of that entity. Assuch, the terms “a” (or “an”), “one or more” and “at least one” can beused interchangeably herein. It is also to be noted that the terms“comprising”, “including”, and “having” can be used interchangeably.

The term “automatic” and variations thereof, as used herein, refers to aprocess or operation done without material human input when the processor operation is performed. However, a processor an operation can beautomatic, even though the performance of the processor operation usesthe material or immaterial human input if the input is received beforeperformance of the processor operation. Human input is deemed to bematerial if such input influences how the process or operation will beperformed. Human input that consents to the performance of the processoroperation is not deemed to be “material”.

The present disclosure includes implementation of an upgraded RADAR unitby incorporating an ultra-lowphase-noise frequency synthesizer to makethe RADAR functioning effectively by transmitting radio signals withmuch lower phase-noise than what is found in traditional RADAR systemson the transmit side. On the receive side of the RADAR system, theultra-lowphase-noise synthesizer adds only a very small amount ofphase-noise to the signal. More specifically, in an embodiment, theupgraded RADAR unit generates a very low amount of phase-noise and thusminimizing the impact of phase-noise on the transmitted and the receivedsignal. The RADAR unit may include a Synthetic Aperture RADAR (SAR), oranother kind of RADAR, for determining information corresponding totargets. Further, the present disclosure may utilize modulated signalsuch as Frequency Modulated Continuous Wave (FMCW) of a type or othermodulated signal for the RADAR Unit. As mentioned above FMCW based RADARis advantageous in terms of power saving Further, in FMCW based RADARunit, various factors such as distance and velocities may be determinedbased on frequency differences from the instantaneous transmitted signalrather than travel time. In most cases, FMCW RADAR signals are processedwith the help of FFT utilizing signal processing windows and pulsecompression algorithms. While these methods are good, the phase-noise ofthe system still remains important since it is a statistical phenomenonthat may be measured and calculated as an average, but instantaneousvalue thereof cannot be determined, thus it cannot be mitigated easilywith existing algorithms. However, its influence on system performancewill drastically be reduced with the collaboration ofultra-lowphase-noise frequency synthesizer. As a result, the overallsystem capability of accuracy and target detection will be vastlyimproved. Further, once distances, return phases and velocities can bedetermined with high accuracy, the data can be used to put together anaccurate 3-dimensional image of the surroundings.

RADAR sensors are generally placed in the middle of the front bumper ofa vehicle. There can be several RADAR units on the front and back bumperof a vehicle. Some of them are operable in the 24 Ghz band and some ofthem are in the 77 GHz band. Each of them may have differentcharacteristics. Together these multi-frequency RADARs can provide moreinformation. In addition, multiple RADARs can also work as SAR, thathelps in getting a better image.

The placement of the RADARs on the vehicle enables features likeforward-collision, collision-warning etc. As described earlier, theRADAR sensors utilize a transmitter to send short pulses ofelectromagnetic radiation. In some models, the transmitter is turned offafter transmission and receiver listens for reflected pulses bouncingoff from objects. However, if the transmitter and the receiver areseparate then there is no need to switch off the transmitter betweenpulses. Integration of the RADAR sensors into the bumper needs to beperformed carefully. Otherwise, there might be interference and accuracyproblems. Therefore, the signals from the RADAR sensor should be of highquality and relatively constant across azimuth of the sensor whileminimizing the wasted energy and delivering to undesired directions orbeing bounced back from bumper itself.

For perfect working of the RADAR sensor, while being placed in thebumper, electrical properties of the bracket where it is to be fittedand the fascia materials are taken to simulate RADAR working. Thesimulations may be done utilizing waveguide or quasi-optical properties.The measured electrical properties may include dielectric constant andloss of tangent of the fascia, bracket of the RADAR sensor and the paintlayer.

RADAR simulations can be employed for designing of single RADARcomponents or develop complete systems including all RADAR installationsand the vehicle. Complete RADAR simulation is a huge task. This taskinvolves the RADAR to cover millions of electrical wavelengths. This isfurther increased by the number of times the RADAR updates centralprocessing, number of antennas used, range and velocity of the RADARsystem, and comparative velocity of environmental factors.

FIG. 1 illustrates a general block diagram of a negative feedback system100. The negative feedback system 100 has an input R and an output C, asummer/comparator 102, a forward path function G 104 and a feedback pathfunction H 106. The summer/comparator 102 compares the input R with asample B of the output C fed back through function H 106, to produce anerror signal E that is relative to the difference between the input Rand the feedback sample B. This error signal E is fed to the mainelement G function 104 in the forward path. If the output signal C tendsto drift upwards, the error signal E pushes it back downwards and viceversa. Thus, the negative feedback system 100 stabilizes the outputsignal C. The negative feedback system 100 finds applications in manysystems for stabilizing frequency, output power, and many otherfunctions.

FIG. 2 illustrates a general block diagram of a standard Phase Lock Loop(PLL) 200. The PLL 200 is a frequency feedback system comprising areference clock 202, a digital phase/frequency detector (PFD) 204, aloop filter 206, a Voltage Controlled Oscillator (VCO) 208, and afrequency divider 210.

The VCO 208 is the main output block in the forward path and is tuned toproduce a frequency as set by a tuned circuit. The VCO 208 has afrequency output Fout that can be changed by a control voltage Vt over apre-set range of frequencies.

The phase detector 204 is a comparator for both the clock input Fclockand the feedback sample from the output Fout divided by divider N 210.The phase detector 204 compares the two input frequencies Fclock andFout/N. When the two input frequencies are not equal, the device 204acts as a frequency discriminator and produces either a negative orpositive voltage, depending on the polarity of the frequency differencebetween the two inputs. When the two input frequencies are the deviceproduces an error voltage Vt relative to the phase difference betweenthe two equal frequencies.

The loop-filter 206, filters and integrates the error signal produced bythe phase detector 204 and feeds it to the VCO 208. The loop filter 206is usually based on passive components like resistors and capacitors,but also in some cases it is a combination of active devices like anoperational amplifier and passive components.

The reference clock 202 is, in general, a low-frequency crystaloscillator signal source that feeds Fclock to the phase detector 204,and to which the output signal Fout is “locked”. The reference clock 202is set at some frequency, for example, a standard frequency 10 MHz. Thelocking “mechanism” transfers some of the qualities of the referenceclock 202 to the main output signal Fout. Its main features usually are:a) frequency stability over temperature—generally in the range of 0.1-5ppm (parts per million), b) accuracy—Can be tuned to very high accuracy,c) very low phase-noise—Its phase-noise is transferred to the outputsignal multiplied by the ratio of 20*LOG(N) where N is the ratio betweenthe output frequency and the clock frequency applied to the phasedetector 204.

The frequency divider 210 is based on digital devices like gates andflip-flops, through which the input frequency Fout is divided by anumber N to produce Fout/N which is fed to the other input of the phasedetector 204. This number N is software controllable. The control signalcomes usually from a microcontroller or from a PC or from anywhere thatbasically will send software control to the frequency divider 210 tochange the division number N. The target of the division number N is toenable the output frequency of the frequency divider 210 to be equal tothe clock frequency of the reference clock 202.

The entire operational procedures of a standard Phase Lock Loop (PLL)200 is as follows: If an input clock signal Fclock is applied, usuallyby a reference clock 202, the phase detector 204 compares the phase andfrequency of the input signal Fclock with that of the VCO 208 divided byN and generates an error voltage Vt that is related to the difference inthe two signals. The error voltage Vt is then filtered and applied tothe control of the VCO 208, thereby varying the VCO 208 frequency in adirection that reduces the frequency difference between the two signals.When the frequencies of the two signals become sufficiently close, thefeedback nature of the system causes the system to lock with theincoming signal. Once in lock, the VCO 208 frequency divided by N isidentical with the input signal Fclock, except for a finite phasedifference which is necessary to generate the corrective error voltageVt to shift the VCO 208 frequency to the input signal frequency Fclock,thus keeping the system in the lock.

When the division number N is changed, say for example by 1, the outputfrequency Fout jumps exactly by a step. In an example, if the referenceclock 202 generates a frequency 1 MHz, then every time the divisionnumber N changes by steps of 1, the output frequency Fout changes byequal steps of 1 MHz.

Like all negative feedback systems, the PLL 200 has a loop bandwidth setby the component parameters and the loop filter 206. In other words, thePLL 200 is a sophisticated frequency multiplier with a built-innarrowband, automatically tuned band-pass filter as the output frequencyFout is basically Fclock multiplied by the number N. The loop bandwidthis also responsible directly for how fast the output frequency of PLL200 may change between different frequencies. The PLL 200 is a devicewhere the VCO 208 is locked to a single clock reference signal which isvery low but also very clean and very stable and the output frequencycan be changed by equivalent steps by controlling the frequency divider210 in the feedback loop.

FIG. 3 illustrates a simplified drawing of a digital phase/frequencydetector 204. A phase detector or phase comparator is a frequency mixer,analog multiplier or logic circuit that generates a voltage signal whichrepresents the difference in phase between two signal inputs. It is anessential element of the phase-locked loop (PLL). A specialized variantthat additionally detects frequency is referred to as Phase FrequencyDetector (PFD). A phase-frequency detector is an asynchronous sequentiallogic circuit which determines which of the two signals has azero-crossing earlier or more often. When used in a PLL application, thelock can be achieved even when it is off frequency. Such a detector hasthe advantage of producing an output even when the two signals beingcompared differ not only in phase but in frequency.

The phase/frequency detector 204 compares two input frequencies Fclockand Fout/N. When the two input frequencies are not equal, it acts as afrequency detector and produces one or zeros to produce a voltagecontrol Vt that pushes corresponding VCO 208 in the direction of thereference. In other words, if the VCO 208 is above the reference thenthe voltage control Vt is high to push the VCO 208 down and vice versa.When the two input frequencies are the same and a frequency lock isachieved, the phase detector 204 acts as a phase detector and comparesthe two phases and continues to produce an error voltage to control thefrequency and phase of the output device.

FIG. 4 illustrates an example of an active filter as applied to ageneral PLL 400. The kind of loop filter i.e. passive filter or activefilter can be chosen on the basis of specific requirement. A passiveloop filter is based on resistors and capacitors only, while an activeloop filter is based on an amplifier and a capacitor-resistor network inthe feedback system. A passive filter is preferred in cases where, areference PLL is of a single frequency and will need only a singlevoltage in order to stay in that single frequency. The other reasonsbeing simplicity, cost and most advantageously no addition of noise, asactive devices tend to add additional noise to the system. However,active filters find more acceptance because of the possibility ofamplification of the input signal. Amplification is made possible by anoperational amplifier employed in the active filter.

The loop filter 206, of FIG. 2, is an active filter that includes anoperational amplifier 402 and a capacitor-resistor network 404 in thefeedback loop. In some instances, the phase detector 204 of the PLL 200may produce voltage up to 5 volts but the corresponding VCO 208 may needa voltage of above 5 volts, say, for example, up to 18 volts in order toreach its complete range, so the active filter 206 facilitates not onlyfiltering but also provides the capability to go to higher voltages.

FIG. 5 illustrates the principle of sample-and-hold mechanism 500. Thefirst sample and hold circuit 502 includes a switch S and a holdcapacitor CH. The operation of the switch S is controlled by the samplecontrol. When the switch S is closed, a voltage sample of the inputfrequency is sampled and when the switch is opened, the voltage sampleis held on the hold capacitor CH.

The second sample and hold circuit 504 includes two buffers A1 and A2with unity gain for isolation purposes, in addition to the switch S andthe hold capacitor CH. The buffer A2 is preferably an electronic buffer,so that the hold capacitor CH does not discharge parasitically betweenconsecutive samples. In other words, the hold capacitor CH holds thevoltage between samples.

FIG. 6 illustrates an example of practical implementation of a combgenerator and sampling phase detector. The schematic shows a StepRecovery Diode (SRD) as comb generator feeding the dual Schottky diodethat acts as a phase detector. The implementation of circuit 600including a Step Recovery Diode (SRD) 602 as a comb generator and thedual Schottky diodes 604 and 606 as a phase detector.

The input to the circuit 600 in this example is a clock input of 100 MHzsine wave. The SRD 602 is a special device that turns the 100 MHz sinewave input into a very narrow pulse train of the same frequency, so itacts as a comb generator. The two Schottky diodes 604, 606 acts asswitches and act as sampling switches. The RF voltage (output from thecorresponding VCO) to be sampled is connected to a point between the twodiodes 604 and 606. The SRD 602 creates an output of positive andnegative pulses. The positive and negative pulses act as control signalsto the diodes 604 and 606 that act like switches. The sampled voltageoutput is an error DC voltage which is created by sampling the RF inputthrough the dual Schottky diodes 604 and 606. The output of the RFsignal is sampled whenever the diodes 604 and 606 are opened by thenarrow pulses coming from the SRD 602. The voltage sample is held on thecapacitors C following the diodes 604 and 606.

FIG. 7 illustrates a schematic of the comb generator and sampling phasedetector with a clock pre-amplifier and two DC buffers following thephase detector. The voltage samples are held on two very smallcapacitors (which are basically the input capacitance of the voltagebuffers, no need for external capacitors) on both sides of the dualdiode pair, so as not to enable the whole capacitor to dischargeparasitically between the samples. These capacitors are buffered by acouple of ultra-low input bias current buffers to prevent dischargebetween samples. The two voltages are summed, fed to a loop filter,whereby the clean Vt is fed to the VCO to control the frequency.

This implementation of sampling phase detector creates an analog phasedetector, very similar to a mixer. The analog sampling phase detectorhas a certain defined locking space or locking distance, and it does notlock from a frequency difference like the phase/frequency digitaldetector. It has some locking range and only within that locking range,the VCO locks by itself on the reference. In a sampling PLL, the VCOdoes not lock on the reference, but on the Nth harmonic of thereference. In other words, one can lock a 9 GHz on the 90th harmonic ofthe 100 Megahertz clock. This is done as the input frequency is sampledevery 100 cycles, not every cycle.

This type of product may contain some “search mechanism” to help lockthe PLL. The most common one involves a positive feedback on the loopfilter itself. While the loop is not locked, the loop filter acts as avery low-frequency oscillator that drives the VCO back and forth acrossthe frequency range. When it passes close enough to the harmonic of theclock, it will lock and stay locked. A nice feature of this mechanism isthat it turns off automatically when the loop locks. This happensbecause of the nature of the loop as a negative feedback system.

However, this type of search mechanism suffers from many problems, itsoperation is subject to temperature changes and it makes this productdifficult to produce, tune and sell successfully.

FIG. 8 illustrates a phase-noise plot 800 of an example free runningVoltage Control Oscillator (VCO) in the frequency domain (spectrumanalyzer), without being locked in a PLL. As said before, Phase-noise isa key element in many RF and radio communications systems as it cansignificantly affect the performance of systems. Phase-noise is thefrequency domain representation of rapid, short-term, randomfluctuations in the phase of a waveform, caused by time domaininstabilities also referred to as “jitter”.

For example, in the frequency domain, where the scales are amplitude vs.frequency, ideally, a frequency of 100 MHz may look like a single linestaying at exactly 100 MHz However, practically with modern equipment inthe laboratory, amplitude vs frequency may not look like a single linebut it will look like a single line with a “skirt” 802 which goes widerand wider as we go down. The phase-noise plot 800 looks like the skirt802 on the left and the right of the exactly desired frequency f_(o).The quality, height, width of the skirt 802 determines how thephase-noise may affect the system or the performance of the system. So,it is desirable to minimize phase-noise as much as possible is toimprove the system performance.

Phase-noise is another term to describe short-term frequencyinstability. The signal generated by a frequency source is neverpractically “clean”. Its frequency is never absolutely stable at thedesired value. It has “Phase-noise” which is frequency shifting, i.e.small frequency shifts at different rates and different amplitudes ofthe main frequency. It changes around the center set frequency f_(o) atdifferent rates and amplitudes. In the time domain, the phase-noise maybe referred to as jitter. Long-term frequency stability is the drift ofthe center frequency over time or over temperature.

FIG. 9 illustrates a phase-noise plot 900 of an example Voltage ControlOscillator (VCO) in the frequency domain (spectrum analyzer),compensated by being locked in a PLL.

The upper line 904 is the free running VCO phase-noise, before it islocked in a PLL, and the lower line 902 is the shaped VCO phase-noise.In the PLL, the principle of locking the VCO to a reference frequencyattenuates the phase-noise of the VCO, in an amount related to the loopbandwidth. Outside the loop bandwidth, the VCO noise remains almost sameas the phase-noise without the PLL, while inside loop bandwidth it isattenuated more and more as offset frequency from the main carrier isreduced. At very high frequency, i.e. above the loop bandwidth, thelocking almost has no effect, as the phase detector correction signal isnot fast enough to reach the VCO for very fast changes or very fastdisturbances. However, inside the loop bandwidth or at low frequencies,the compensated phase-noise of the VCO is much lower than that of thefree running VCO. All the frequencies that are close to the center ofthe frequency fo are easy to detect and compensate.

FIG. 10 illustrates two plots 1000: (a) a simulation of phase-noise ofan example PLL, and (b) an actual measurement. FIG. 10 (a) illustrates asimulation graph of phase-noise of an example PLL. The simulation graphshows the overall phase-noise of the example PLL and includes thecontribution of all the components that contribute to the phase-noise.The simulation graph illustrates first, second and third regions 1002,1004 and 1006 of the phase-noise. The first region 1002 which is veryclose to the carrier depicts a steep line which basically comes from thereference clock such as the Temperature Controlled Crystal Oscillator(TCXO, or other reference clock device). The first region depicts thenoise of the TCXO, multiplied by 20 log N, where N is the ratio ofoutput frequency to the clock frequency. The second region 1004 depictsa flat phase-noise which is basically the noise floor of the digitalphase detector multiplied by the same ratio of 20 log N. The thirdregion 1006 depicts a steep line which is the inherent VCO phase-noisenot affected by the loop bandwidth and locking phenomenon. The dashedline 1008 depicts the VCO “corrected” phase-noise inside loop bandwidth.Below the flat area, the compensated VCO phase-noise does not affect theoverall result because it is way below the noise floor of the phasedetector multiplied by that ratio. The actual measurement of phase-noiseof an example PLL is illustrated in FIG. 10 (b). One can see clearly thesimilarity between the two curves.

FIG. 10 illustrates a phase-noise plot 1100 of a closed loop PLL,showing clearly the effect of the phase detector multiplication number20*LOG(N) within loop bandwidth. The phase-noise plot 800 illustratesphase-noises on both sides of the carrier frequency fo, where the leftside is a mirrored image of the right side. The phase-noises on bothsides of the carrier folook like it is passing through a band-passfilter.

As illustrated, on both sides, the in-band phase-noise inside the loopbandwidth is flat in shape and is equal to the phase detector and/or thereference clock noise multiplied by 20 log N. At the point of the loopbandwidth, the phase-noise goes up before going down again. This is dueto the addition of 3 dB due to a combination of phase-noise of the freerunning VCO and the phase detector. The upper straight line 1102 depictsa phase-noise contributed by the phase detector at N1 and the lowerstraight line 1104 depicts a phase-noise contributed by the phasedetector at N2. It can be seen that there is the difference inphase-noise in the flat area, due to two different “N” numbers. Thephase detector contributes a higher in-band phase-noise at a highervalue of N.

Thus, in order to achieve low phase-noise, it is essential to: a) choosecomponents such as phase detector and reference clock with the lowestinherent phase-noise possible, and b) lower the ratio number N as muchas possible.

FIG. 11 illustrates plot 12902200 of measurement in terms of phase-noisein 1 Hz bandwidth at a Δf offset frequency from the carrier. Thephase-noise expression is usually in dBc, i.e. dB relative to thecarrier c power level Ps, in other words how low it is compared to thecarrier per Hz, in a bandwidth of 1 Hz. That is basically the term thatis used for phase-noise, dBc per Hertz (dBc/Hz) at a certain Δf from thecarrier.

As an example for the measurement method, suppose ΔF is 10 KHz, thephase-noise power level Pss is measured at the level of −70 dBm on thespectrum analyzer, and the carrier power level Ps is measured at thelevel of 10 dBm, the ratio between the Ps 10 dBm and the PssB −70 dBm at10 KHz from the carrier is therefore 80 dB, so the phase-noise at 10 KHzoffset from carrier and is −80 dBc/Hz.

For many systems, the important parameter to evaluate performance is notthe phase-noise measured at a single frequency offset from the carrier,but the integrated phase-noise from one offset frequency to another one.Following are four different equations and terms to define integratedphase-noise:

∫L(f)df${S_{phi}(f)} = {{{( \frac{180}{\pi}\; ) \cdot \sqrt{2 \cdot {\int{{L(f)}{df}}}}}{S_{nu}(f)}} = {{\sqrt{2 \cdot {\int{{{L(f)} \cdot f^{2}}{df}}}}{S_{y}(f)}} = ( \frac{s_{nu}(f)}{f_{osc}} )}}$Where the first equation describes single sideband phase-noise [dBc]The 2^(nd) equation describes the spectral density of phase modulation,also known as RMS phase error (degrees)The 3^(rd) equation describes the spectral density of frequencyfluctuations, also known as RMS frequency error or residual FM (Hz)The 4^(th) equation describes the spectral density of fractionalfrequency fluctuations

For example, the first equation defines the Phase-noise in dBc. It canbe translated by the 2nd equation to degrees (relevant in respect oflearning modulation schemes). As per further equations, the phase-noisecan also be translated in terms of Hz and time domain phase jitterseconds.

FIG. 13 illustrates a general block diagram 1300 of an example dual loopPLL. The main target of the dual loop design is to reduce themultiplication number N in the main PLL.

The dual loop PLL 1300 includes an upper PLL 1302, referred to as a mainPLL 1302, and a lower PLL 1304, referred to as a reference PLL 1304, aTCXO 1306 operating as a master clock, feeding a clock signal Fc to boththe primary PLL 1302 and the reference PLL 1304.

The reference PLL 1304 includes a first phase detector 1314, and asingle frequency first VCO 1316 that operates at a reference frequencyFr. The reference frequency Fr is fed to the first input of a downconvert mixer 1312.

The main PLL 1302 includes a second phase detector 1308 and a second VCO1310 that generates an output frequency range from F1 to F2. A sample ofthe output frequency range F1 to F2 is fed to the second input of thedown-convert mixer 1312 and mixed with a single reference frequency Fr.The output from the down-convert mixer 1312 is at a much lower frequency(F1 to F2)−Fr. This lowered frequency is fed back to the second phasedetector 1308 through a frequency divider 1318 of value N1.

Therefore: a) Without the down-convert mixer 1412: F1 to F2=NxFc, b)With the down-convert mixer 1312: (F1 to F2)−Fr=N1×Fc. As a result,there is a reduction in the number N: N1/N=((F1 to F2)−Fr)/(F1 to F2).

The N1 number is basically the division number that the frequencydivider 1318 will use to divide the output of the mixer 1312 and feed tothe second phase detector 1308. The value of N1 is set as minimal, asthe output from the mixer 1312 is at a much lower frequency thanoriginal frequency range F1 to F2.

To give an example: a) Suppose Fc=1 MHz, b) Suppose F1 to F2=10,000 to11,000 MHz Then N=10,000 to 11,000. Now If Fr=9000 MHz, then((F1-F2)−Fr)=1000 to 1900 MHz Then N1=1000 to 1900. Thus, the value of Nis reduced from 11,000 to 1900. In dB, it is a ratio of 15 dB. Thismeans, that the phase-noise is reduced by a factor of 15 dB.

The disadvantage of the example dual loop design is that while nicelyreducing the number N in the main PLL, the reference PLL, containing adigital phase/frequency detector becomes the main factor contributing tothe overall output phase-noise.

FIG. 14 illustrates a general block diagram 1400 of an example samplingPLL. The sampling PLL 1400 includes a TCXO 1402, a comb generator 1404,a sampling phase detector 1406, a loop filter 1408, and a VCO 1410. Thesampling PLL 1400 does not include digital phase/frequency detector anda frequency divider. Thus, no digital noise floor is generated that canbe multiplied and affect the performance of the system.

The TCXO 1402 feeds the clock signal Fclock to the comb generator 1404.The comb generator 1404 is a device that changes the input sine wavesignal at frequency Fclock to an output signal of very narrow pulses atthe same frequency as the input sine wave signal.

The pulse output from the comb generator 1404 is used as a controlsignal to the sampling phase detector 1406. The sampling phase detector1406 receives an RF signal of frequency Fout from the VCO 1410 andincludes two diodes acting as switches to sample the RF signal byopening and closing the diodes based on the narrow pulses from the combgenerator 1404. The sampled voltage Vt produced is “held” on capacitorsand buffered until the next sample period. The voltage samples arealways at the same level; thus, a DC voltage Vt is generated by thesampling phase detector 1406. The loop filter 1408 cleans and filtersthe DC voltage Vt and provides it to the VCO 1410 to control the VCOfrequency Fout. Fout=Fclock*N, where N is the Nth spectral harmonic linein the “comb” spectrum.

FIG. 15 illustrates block diagram 1500 depicts how the impulse or “comb”generator 1404 changes a wave shape of a signal from sine wave 1502 tonarrow pulses in 1504. A frequency source 1506 generates the input sinewave 1502 of frequency F1 and time period T1.

The comb generator 1404 turns the input sine wave 1502 to a series ofvery narrow pulses 1504 with same time period T1, and a pulse bandwidthas tp in the time domain. For example, if the frequency of input sinewave 1502 is 100 MHz, then the impulse train generator 1508 generates aseries of very sharp narrow pulses 1504 of the same frequency.

FIG. 16 illustrates an example output 1600 of a comb generator 1404 inthe frequency domain. In the frequency domain (spectrum analyzerscreen), the output 1600 of the comb generator 1404 looks like a “comb”,i.e. a row of lines extending up to very high frequency. In theory, ifthe bandwidth of the clock pulse is infinitesimal, the row of linesappears with equal amplitude to infinity. The output 1600 looks like aseries of lines, with the spacing between the lines same as the initialfrequency. In an example, if the initial frequency is 1 GHz, thespectrum of lines is 1 GHz apart.

FIG. 17 illustrates a block diagram 1700 of an ultra-low phase-noisefrequency synthesizer as suggested in a first embodiment. The ultra-lowphase-noise frequency synthesizer 1700 includes two Phase Lock Loops(PLLs). One is a main PLL 1710 and the other one is a reference PLL1718. The main PLL 1710 comprises a high-frequency low noise DirectDigital Synthesizer (DDS) 1702 to generate at least one clock signal Fc2of the variable frequency range. The high-high-high-frequency low noiseDDS 1702 generates the at least one clock signal Fc2 of variablefrequency range by taking input from at least one software controllableinstructions and at least one DDS clock signal. The frequency of the atleast one clock signal Fc2 is always lower than the frequency of the atleast one DDS clock signal. The at least one DDS clock signal isgenerated by a first fixed frequency divider 1714. The high-frequencylow noise DDS 1702 forwards the generated at least one clock signal Fc2of variable frequency range towards a Digital Phase Frequency Detector1704.

The Digital Phase Frequency Detector 1704 compares two signals comingfrom two directions and generates at least one signal. One signal is theat least one clock signal Fc2 of variable frequency range generated bythe high-high-high-frequency low noise DDS 1702. The second signal is atleast one signal of frequency Fif/N1 generated by a second fixedfrequency divider 1712. The Digital Phase Frequency Detector 1704compares these two signals and generates at least one first controlvoltage Vt1 and forwards it towards a primary Voltage Control Oscillator(VCO) 1706. The primary Voltage Control Oscillator (VCO) 1706 generatesat least one output signal of frequency Fout from the received at leastone first control voltage Vt1. The main PLL 1710 further comprises adown convert mixer 1716.

The primary role of the reference PLL 1718 is to help the main PLL 1710in reducing the phase-noise present in the at least one output signalFout. The reference PLL 1718 comprises a reference clock (for example aTemperature Compensated Crystal Oscillator (TCXO)) 1724 to generate atleast one first clock signal of a fixed single frequency Fc1. Further,the reference PPL comprises a sampling phase detector 1722 (thatincludes the comb generator and the sampling phase detector) to generateat least one-second control voltage Vt2 and a reference Voltage ControlOscillator (VCO) 1720.

One important thing to notice here is that unlike other dual loopdesigns, the reference PLL 1718 uses the sampling phase detector 1722.The reference PLL 1718 does not use kind digital devices like theDigital Phase Frequency Detector 1704, or the first fixed frequencydivider N1 1714. Simultaneously the reference clock 1724 present in thesampling PLL 1718 is also a very low noise generating device. Due tothese reasons, the contribution of phase-noise from the reference PLL1718 to the main PLL 1710 becomes close to negligible. The referenceVoltage Control Oscillator (VCO) 1720 generates at least one referencesignal Fr and forwards it towards the down-convert mixer 1716. Thereference PLL 1718 plays a major part in all relevant communications andsimilar systems by being part of various frequency synthesizers, andalso as a standalone frequency source for all the systems of up and downconversion processes in the same equipment.

The down-convert mixer 1716 receives at least one reference signal offrequencies Fr and at least one output signal of frequency Fout andgenerates at least one intermediate signal of frequency Fif and forwardsit towards a second fixed frequency divider 1712. The second fixedfrequency divider 1712 generates at least one signal of frequenciesFif/N1 by dividing the incoming at least one signal of frequency Fif bya predefined factor. The second fixed frequency divider 1712 forwardsthe generated at least one signal of frequencies Fif/N1 towards theDigital Phase Frequency Detector 1704. The primary VCO 1706 forwards theat least one output signal Fout towards a fixed frequency multiplier1708 to generate at least one final output signal Fout final.

It is important to notice that frequency divider 1712 is optional andthe main PLL can operate without division of Fif.

To explain the above-disclosed disclosures with an example let's say thereference clock 1724 generates the at least one first clock signal of afixed single frequency Fc1 100 MHz. The sampling phase detector 1722generates the second control voltage Vt2 by sampling the at least onefirst clock signal of a fixed single frequency Fc1 100 MHz and forwardsthe sampled values of the at least one first clock signal of a fixedsingle frequency Fc1 100 MHz towards the reference Voltage ControlOscillator (VCO) 1720. The reference Voltage Control Oscillator (VCO)1720 generates the at least one reference signal Fr and forwards ittowards the down-convert mixer 1716. In an example, the reference VCO1720 generates a frequency of 9.4 GHz.

In the example, the first frequency divider 1714 divides the generatedreference signal of frequency 9.4 GHz by a predefined factor of 3 togenerate the at least one DDS clock signal. The high-high-frequency lownoise DDS 1702 receives the at least one DDS clock signal, and based onthe at least one software controllable instructions, generates the atleast one clock signal Fc2 of the variable frequency range from 0.1 GHzto 0.225 GHz.

In the example, the primary VCO 1706 generates the at least one outputsignal of frequency Fout ranging from 9.5 GHz to 9.625 GHz. Thedown-convert mixer 1716 mixes the at least one output signal offrequency Fout ranging from 9.5 to 9.625 GHz with the reference signalFr at frequency 9.4 GHz to generate the at least one intermediate signalFif having frequency ranges from 0.1 GHz to 0.225 GHz. Since the atleast one clock signal Fc2 ranges from 0.0.1 GHz to 0.225 GHz, thesecond fixed frequency divider 1712 is set to divide the at least oneintermediate signal Fif by a predefined factor of 1, (which meanspractically no divider needed in this case) to generate the at least onesignal of frequencies Fif/2 ranging from 0.1 GHz to 0.225 GHz.

The fixed frequency multiplier 1708 multiplies the at least one outputsignal Fout ranging from 9.4 GHz to 9.625 GHz by a predefined factor of8 to generate the at least one final output signal Foutfinal rangingfrom 76 GHz to 77 GHz. It is easier and relatively inexpensive toimplement the chip design of the frequency synthesizer 1700 for outputfrequencies 9.4 GHz to 9.625 GHz, and then multiply the at least oneoutput signal Fout by 8 to generate the at least one final output signalFoutfinal in the range of 76 GHz-77 GHz.

The down-convert mixer 1716 lowers the frequency of the at least oneoutput signal Fout, to reduce the ratio of the frequencies of the secondclock signal and the feedback signal. Instead of feeding the at leastone output signal Fout directly to the Digital Phase Frequency Detector1704, it is mixed down to create at least one signal with a much lowerfrequency and obtain a much lower value of the second fixed frequencydivider 1712 or it is not needed as in this example.

As the primary phase-noise present in the ultra-low phase-noisefrequency synthesizer 1700 is due to the product of the noise present inthe high-frequency DDS 1702 and the second fixed frequency divider 1712,the less the value of the second fixed frequency divider 1712 will be,the less will be the generated phase-noise in the ultra-low phase-noisefrequency synthesizer 1700. Therefore, when the second fixed frequencydivider 1712 is equal to 1, the DDS signal noise is multiplied by thenumber 1 which means it is transferred as is to the output and thisachieves a very ultra-low noise.

The reduction in the ratio of the frequencies leads to a reduction in aphase-noise of the final output signal Foutfinal. The comparisonfrequency is much lower, so that the number N by which the noise ismultiplied inside the main PLL 1710 is much lower. In an example, theeven if the ratio of second fixed frequency divider=2 reduces thephase-noise of the final output signal Foutfinal by a factor of 20-40 dBcompared to a single PLL design. For example, phase-noise at 100 KHz Δffrom the carrier with standard PLL synthesizers is approximately −106dBc/Hz. With the proposed frequency synthesizer 1700, the phase-noise at100 KHz Δf from the carrier could be in the range of −130-135 dBc/Hz,causing a significant improvement of 24-29 dBs

To summarize, the drastic improvements achieved in reducing phase-noisein the ultra-low phase-noise frequency synthesizer 1700 is based on thefollowing: a) use of Dual PLL approach to reduce the multiplicationnumber N2, b) use of sampling PLL 1718 as the reference PLL, to make itsnoise contribution and reference PLL phase-noise negligible, c) use ofDDS 1702 to provide low noise, high frequency input to the main PLL1710, and d) use of high-frequency Digital Phase Frequency Detector 1704in the main PLL 1710.

In this embodiment, the ultra-low phase-noise frequency synthesizer 1700is implemented in form of a module. In another form of this embodiment,this design of the ultra-low phase-noise frequency synthesizer 1700 canbe implemented not only as a part of the big module, but also as anindependent, separate chip, which can become a part of the front-endmodule of a RADAR transceiver. The synthesizer can be implemented in anadvanced technology for example but not limited to, like SiGe or GaAs.

FIG. 18 illustrates a block diagram 1800 of an ultra-low phase-noisefrequency synthesizer as suggested in a second embodiment. The lowphase-noise frequency synthesizer 1800 includes two Phase Lock Loops(PLLs). One is a main PLL 1812 and the other one is a reference PLL1818. In this embodiment, the ultra-lowphase-noise frequency synthesizer1800 comprises one single reference clock (for example a TemperatureCompensated Crystal Oscillator) 1802 which provides input clock signalsto both the main PLL 1812 and the reference PLL 1818.

The main PLL 1812 comprises of a Fractional-N synthesizer chip 1804, aprimary Voltage Controlled Oscillator (VCO) 1810 and a down convertmixer 1816. The Fractional-N synthesizer chip 1804 includes ahigh-frequency Digital Phase Detector 1806 and a software controllablevariable frequency divider N1 1808.

The reference clock 1802 forwards the generated at least one clocksignal of fixed frequency Fc towards the high-frequency Digital PhaseDetector 1806 which is located inside the Fractional-N synthesizer chip1804. On one hand, the high-frequency Digital Phase Detector 1806receives the at least one clock signal of fixed frequency Fc. On theother hand, the high-frequency Digital Phase Detector 1806 receives atleast one signal of frequency Fif/N1 generated by the softwarecontrollable variable frequency divider N1 1808. The high-frequencyDigital Phase Detector 1806 compares these two signals, generates atleast one first control voltage Vt1 and then forwards the generated atleast one first control voltage Vt1 towards the primary VCO 1810. Theprimary VCO 1810 generates at least one output signal of frequency Foutfrom the received at least one first control voltage Vt1.

The primary role of the reference PLL 1818 is to help the main PLL 1812to reduce the phase-noise present in the at least one output signalFout. The reference PLL 1818 comprises a sampling phase detector 1822and a reference Voltage Control Oscillator (VCO) 191820.

One important thing to notice here is the application of the samplingphase detector 1822. The sampling PLL 1818 does not use kind digitaldevices like the Digital Phase Detector 1806, or the softwarecontrollable variable frequency divider N 1808. Due to these reasons,the contribution of phase-noise from the sampling PLL 1818 to the mainPLL 1812 becomes close to negligible.

The sampling phase detector 1822 receives the same at least one clocksignal of fixed frequency Fc generated by the reference clock 1802,generates at least one-second control voltage Vt2 and forwards ittowards the reference VCO 191820. The reference VCO 191820 generates atleast one reference signal Fr and forwards it towards the down-convertmixer 1816.

The down-convert mixer 1816 based on the received at least one referencesignal of frequency Fr and the at least one output signal of frequencyFout generates at least one intermediate signal of frequency Fif andforwards it towards the software controllable variable frequency dividerN1 1808 located inside the Fractional-N synthesizer chip 1804. Thesoftware controllable variable frequency divider N1 1808 generates atleast one signal of frequencies Fif/N1 by dividing the incoming at leastone intermediate signal of frequency Fif by at least one variable valueof N1. The Fractional-N synthesizer chip 1804 varies the value of N1 byexecuting appropriate software instructions. The software controllablevariable frequency divider N1 1808 then forwards the generated at leastone signal of frequency Fif/N1 towards the Digital Phase Detector 1806.

The primary VCO 1810 forwards the at least one output signal Fouttowards a first fixed frequency multiplier 1814 and generate at leastone final output signal Foutfinal by multiplying the at least one outputsignal Fout by a predefined factor.

To explain the second embodiment with an example let's say the reference1802 generates the at least one clock signal of fixed frequency Fc 100MHz to Both the main PLL 1812 and the reference PLL 1818. Thephase-noise of the reference PLL 1818 is generally very low due to theprinciple of sampling and also to the presence of the input referenceclock 1802 which is itself a very low noise generating device.

The sampling phase detector 1822 generates the second control voltageVt2 based on the at least one clock signal of fixed frequency Fc 100 MHzand forwards the second control voltage Vt2 towards the reference VCO191820. The reference VCO 191820 generates at least one reference signalFr and forwards it towards the down-convert mixer 1816. In an example,the reference VCO 191820 generates reference signals of frequency 9.4GHz.

In the example, the primary VCO 1810 generates the at least one outputsignal of a frequency Fout ranging from 9.5 GHz to 9.6257 GHz. Thedown-convert mixer 1816 mixes the at least one output signal offrequency Fout ranging from 9.5 GHz to 9.625 GHz with the referencesignal of frequency 9.4 GHz to generate the at least at least oneintermediate signal of frequency Fif ranging from 0.1 GHz to 0.225 GHz.

Based on the at least one clock signal of fixed frequency Fc, theFractional-N synthesizer chip 1704 determines the value of the softwarecontrollable variable frequency divider N1708, so as to generate atleast one feedback signal of frequency Ff=Fif/N1.

The frequency range 9.5 GHz to 9.625 GHz easier and relativelyinexpensive to implement the chip design of the low phase-noisefrequency synthesizer 1800, and then multiply the output frequencies by8 in frequency multiplier 1814 to obtain the final output frequencies inthe range of 76 GHz-77 GHz.

The down-convert mixer 1816 lowers the frequency of the output signalFout, to reduce a ratio of frequencies of the second clock signal andthe feedback signal. Instead of feeding the output frequency Foutdirectly to the Digital Phase Detector 1806, it is mixed down to createa much lower frequency, and thus a much lower value of N1. A reductionin the ratio of the at least one clock signal of frequency Fc and the atleast one feedback signal of frequency Ff leads to a reduction in aphase-noise of the final output signal Foutfinal. The feedback frequencyis lowered down, so that the number N1 by which the noise is multipliedinside the main PLL 1812 is also lowered down. If the output frequencyFout is in the range of 9.5 GHz, and it has to be compared with a clockof 100 MHz, the ratio N of 9.5 GHz and 100 MHz is around 95, but if theoutput frequency Fout is mixed down to about 0.2 GHz by the down-convertmixer 1816, then the ratio N1′ of 0.2 GHz and 100 MHz maybe only 2instead of 95 thereby significantly reducing the phase-noise of the lowphase-noise frequency synthesizer 1800.

The improvement in the phase-noise of the low phase-noise frequencysynthesizer 1800 is based on following: a) use of dual PLL to reduce themultiplication number N, b) use of sampling PLL 1818 as the referencePLL to make its noise contribution negligible, c) use of high frequencylow noise reference clock 1802 to provide high frequency input to themain PLL 1812, d) use of high frequency Fractional-N synthesizer 1814 inthe primary PLL 1806.

In this second embodiment, the ultra-low phase-noise frequencysynthesizer 1800 is implemented in form of a module. In another form ofthis embodiment, this design of the ultra-low phase-noise frequencysynthesizer 1800 can be implemented not only as a part of the bigmodule, but also as an independent, separate chip, which can become apart of the front-end module of a transceiver. The ultra-low phase-noisefrequency synthesizer 1800 can also be implemented in advancedtechnology for example like SiGe or GaAs.

FIG. 19 illustrates a block diagram 1900 of the sampling Phase Lock Loop(PLL) system as suggested in a third embodiment. The sampling PLL system1900 includes a Temperature Compensated Crystal Oscillator (TCXO) 1902as an example of the reference clock, a comb generator 1904, a samplingphase detector 1906, a two-way DC switch 1908, a loop filter 1910, aVoltage Controlled Oscillator (VCO) 1912, and a Digital Phase FrequencyDetector 1914. The TCXO 1902 is configured to generate at least oneclock signal of frequency Fc z, which is applied to both of the combgenerator 1904 and the Digital Phase Frequency Detector 1914. Thesampling PLL system 1900 contains two PLL loops. One is a Sampling PLLloop 1916 and the other is a Digital PLL loop 1918.

The principle of operation in this embodiment is this: Initially, thetwo-way DC switch 1908 remaining closed with the Digital Phase FrequencyDetector 1914. Due to this only the Digital PLL loop 1918 is remainsoperational and the VCO 1912 gets locked to the at least one clocksignal of frequency Fc generated by the reference clock TCXO 1902. TheDigital Phase Frequency Detector 1914 also generates at least one lockdetect signal Vld.

Once VCO 1912 gets locked to the at least one clock signal of frequencyFc generated by the reference clock TCXO 1902, the at least one lockdetect signal Vid generated by the Digital Phase Frequency Detector 1914changes the two-way DC switch 1908 to the Sampling PLL loop 1916. Due tothis the Sampling PLL loop 1916 gets closed and the Digital PLL loop1918 gets opened. Since the VCO 1912 is already locked at the correctfrequency, the Sampling PLL loop 1916 will remain closed. One importantthing to notice here is that the loop filter 1910 is common to both theSampling PLL loop 1916 and the Digital PLL loop 1918. As the loopfilter, 1910 is made up of a plurality of resistors and capacitors whichare charged to the right tuning voltage Vt which is applied to the VCO1912. When the Sampling PLL loop 1916 gets closed and the Digital PLLloop 1918 gets opened, the plurality of resistors and capacitors presentin the loop filter 1910 do not change their tuning voltages in thatstep. In other words, the Digital PLL loop 1918 is used to lock the VCO1912 with the exact right frequency generated by the TCXO 1902 and theSampling PLL loop 1916 is used to achieve low phase-noise.

The two-way DC switch 1908 is configured to be switched between thesampling phase detector 1906 and the Digital Phase Frequency Detector1914 based on a status of the lock detect signal Vld generated by theDigital Phase Frequency Detector 1914. For example, the two-way DCswitch 1908 is configured to be connected to the Digital Phase FrequencyDetector 1914 when the lock detect signal Vld islow and configured to beconnected to the sampling phase detector 1906 when the lock detectsignal Vld is high.

In the third embodiment, when the lock detect signal Vld is low, thetwo-way DC switch 1908, the loop filter 1910, the VCO 1912 and theDigital Phase Frequency Detector 1914, form a Digital PLL loop 1918.Whereas, when the lock detect signal Vld is high, the comb generator1904, the sampling phase detector 1906, the two-way DC switch 1908, theloop filter 1910, and the VCO 1912 forms a sampling PLL loop 1916.

As said, initially, the two-way DC switch 1908 is connected to theDigital Phase Frequency Detector 1914, as the lock detect signal Vld islow due unlock state. In the Digital PLL loop 1918, the Digital PhaseFrequency Detector 1914 generates a first DC output signal Vtd based ona comparison of the at least one clock signal of frequency Fc, and atleast one output signal of frequency Fr, the loop filter 1910 filtersthe first DC output signal Vtd and generates the control voltage Vt, andthe VCO 1912 generates the output signal frequency based on the controlvoltage Vt. In an example, the VCO 1912 is configured to generate eitheran output signal of frequency Fr of 11.75 GHz or 12.75 GHz chosen bysoftware control to the Digital PLL loop 1918.

As soon as the Digital PLL loop 1918 is locked at the output frequencyFr, the lock detects signal Vld turns high, the two-way DC switch 1908disconnects from the Digital Phase Frequency Detector 1914 and connectsto the sampling phase detector 1906, forming the sampling PLL loop 1916.

So once locked, the lock detector signal Vld from the Digital PhaseFrequency Detector 1914 controls the two-way DC switch 1908 to switch tothe sampling PLL 1916. The loop filter 1910 contains plurality ofcapacitors and resistors that are already charged to the correct tuningvoltage Vt of the VCO 1912, and since voltage on the plurality ofcapacitors and resistors cannot change in a “jump”, there would not be atransient, and the VCO 1912 may continue receiving the same controlvoltage Vtd. The sampling PLL system 1900 remains locked at the samefrequency but now through the sampling phase mechanism.

In the Sampling PLL loop 1916, the comb generator 1904 receives the atleast one clock signal of frequency Fc and generates at least one combsignal Fcomb. The at least one comb signal Fcomb is basically aplurality of narrow pulses, which are repeating at the same frequency Fcwhich is the frequency of the at least one clock signal generated by theTCXO 1902. The sampling phase detector 1906 after receiving the at leastone comb signal Fcomb generates a second DC output signal Vts based onthe at least one comb signal Fcomb. The loop filter 1910 generates thecontrol voltage Vt based on the second DC output signal Vts and the VCO1912 remains locked at the output frequency Fr based on the controlvoltage Vt.

At the execution of lock by the Digital Phase Frequency Detector 1914,the first DC output signal Vtd becomes equal to the second DC outputsignal Vts. Further, the loop filter 1910 is common to the sampling PLLloop 1916 and the Digital PLL loop 1918 so as to maintain a similarcontrol voltage Vts while switching from the Digital PLL loop 1918 tothe sampling PLL loop 1916 and vice versa.

Another feature is that if by a chance, the sampling PLL loop 1910 losesa lock with the phase of the clock signal, the lock detects signal Vld,which is still active, turns low to re-connect the two-way DC switch1908 to the Digital Phase Frequency Detector 1914 to enable re-lockingof the Digital PLL loop 1918 to the clock signal.

In this embodiment, the sampling PLL system 1900 is implemented in anindependent chip form, with digital circuits replacing analog functions.The sampling PLL system 1900 may also be implemented as a block on asystem on chip (SoC) or as a part of a module. The sampling PLL system1900 may also be used in the ultra-low phase noise frequencysynthesizers 1700 and 1900.

In this embodiment, the Digital PLL loop 1918 always locks at thecorrect frequency as the Digital PLL loop 1918 is software controlled tolock at a right frequency. The Digital Phase Frequency Detector 1914 isalways able to lock from a distance regardless of how far away initiallythe VCO 1912 is from the reference clock Fc. Thus, use of the DigitalPLL loop 1918 in the sampling PLL system 1900 overcomes the problem ofthe sampling PLL loop 1916 not being able to lock outside the lockrange. The Digital PLL loop 1918 is used to lock the VCO 1912 on theright frequency and then switch to the sampling PLL loop 1916 to achievethe low noise. It also enables the system to operate with a wideband RFVCO 1912 with the assurance that it will lock at the correct frequency.It eliminates the unreliable search mechanism and assures lock under allconditions and temperature conditions by providing true lock detectindication. The presence of Digital Phase Frequency Detector 1914enables the use of wideband VCO 1912 in the sampling PLL loop 1916, asthe Digital Phase Frequency Detector 1914 is able to lock the VCO 1912at the desired frequency. The sampling PLL system 1900 offers asignificant improvement over other product and is highly useful as oneof the most important building blocks for ultra-low noise synthesizers.

In the sampling PLL loop 1916, there is no digital noise floor and thereference clock Fc determines the overall phase-noise, as this is theonly factor that is translated to the output frequency by 20 log N.

Advantages of the sampling PLL system 1900: a) It enables the samplingPLL 1916 to operate with a wideband RF VCO with assurance that it willlock at the correct frequency, b) It eliminates the unreliable searchmechanism and assures lock under all offset and temperature conditions,c) It provides true lock detect indication, d) Reliable improvedoperation and performance of the sampling PLL 1916, e) Ultra-low noise,f) Highly reliable, g) Having vastly improved performance, h) Easy tomanufacture and use, i) Operational in a broadband RF range, and j)Implementable in a chip form.

FIG. 20 illustrates an example of a phase-noise simulation plot 2000contributed by a DDS chip in accordance with the first embodiment of thepresent invention. The two-dimensional phase-noise simulation plot 2000comprises of an ordinate (vertical axis) disclosing Phase-noise (dBc/Hz)2002 and one abscissa (horizontal axis) disclosing Frequency (Hz) 2004.The phase-noise simulation plot 2000 discloses four phase-noise plotscorresponding to four input frequencies which are 1396 MHz 2006, 696 MHz2008, 427 MHz 2010 and 171 MHz 2012 generated by the single DDS chip.

In the first embodiment of the present disclosure as disclosed above inFIG. 17, the DDS 1702 element generates at least one clock signal Fc2 ofa variable frequency range of 0.1 GHz to 0.225 GHz. Correlating thisvariable frequency range of 0.1 GHz to 0.225 GHz applicable in the firstembodiment of the present disclosure with the DDS phase-noise simulationplot 2000, it becomes evidently clear that even in worst case scenariothe DDS phase-noise contribution in the first embodiment of the presentdisclosure stays in between the 427 MHz 2010 and the 171 MHz 2012 whichis in between −125 dBc/Hz and −120 dBc/Hz which is really very low.

FIG. 21 illustrates a phase-noise simulation plot 2100 contributed bythe main PLL 1710 in accordance with the first embodiment of the presentdisclosure. The two-dimensional phase-noise simulation plot 2100comprises of an ordinate (vertical axis) disclosing Phase-noise (dBc/Hz)2102 and one abscissae (horizontal axis) disclosing Frequency (Hz) 2104.The phase-noise simulation plot 2100 discloses the phase-noisecontributed by the main PLL 1710 as disclosed in the first embodiment ofthe present disclosure in FIG. 17. It is evidently visible that thephase-noise simulation plot 2100 has multiple contributors. The two mostimportant contributors of phase-noise in the phase-noise simulation plot2100 are the primary VCO 1706 and the DDS 1702 as discussed in FIG. 17.

A phase-noise plot 2108 is the contribution of the primary VCO 1706 inthe phase-noise simulation plot 2100. As the primary VCO 1706 belongs tothe main PLL 1710, the main PLL 1710 attenuates the phase-noise 2108coming from the primary VCO 1706 to quite an extent. This attenuation isclearly visible in the phase-noise simulation plot 2100.

The other primary contributor in the phase-noise simulation plot 2100 isthe phase-noise coming from the DDS 1702 present in the first embodimentof the present disclosure. A phase-noise plot 2112 is the contributionof the DDS 1702 into the main PLL 1710. The phase-noise plot 2112 istitled as XTAL in the phase-noise simulation plot 2100. This phase-noiseplot 2112 is the contribution of the DDS 1702 in the main PLL 1710 atthe worst point of an output frequency of 1000 MHz

The main PLL 1710 forwards the primary VCO 1706 generated outputfrequencies of 9.5 GHz-9.625 GHz towards the down-convert mixer 1716.The down-convert mixer 1716 mixes incoming the primary VCO 1706generated output frequencies of 9.5 GHz-9.6257 GHz with the samplingreference frequency of 9.4 GHz and generates attenuated intermediatefrequencies of 0.1 GHz to 0.225 GHz. This attenuation procedure itselfreduces the phase noise contributions coming from the primary VCO 1706and the DDS 1702. It can be further note that a phase detector noisefloor plot 2114 is negligible.

FIG. 22 illustrates a phase-noise simulation plot 2200 contributed by areference sampling PLL when a TCXO clock (or another reference clock)generates input frequencies of 100 MHz in accordance with the firstembodiment of the present disclosure.

The two-dimensional phase-noise simulation plot 2200 comprises of anordinate (vertical axis) disclosing Phase-noise (dB c/Hz) 2202 and oneabscissae (horizontal axis) disclosing Frequency (Hz) 2204. Thephase-noise simulation plot 2200 discloses the phase-noise contributedby the reference sampling PLL 1718 as disclosed in the first embodimentof the present disclosure in FIG. 17. It is evidently visible that thephase-noise simulation plot 2200 has multiple contributors. The two mostimportant contributors to phase-noise in the phase-noise simulation plot2200 are the reference VCO 1720 and the reference clock TCXO 1724 asdiscussed in FIG. 17.

A phase-noise plot 2208 is the contribution of the reference VCO 1720 inthe phase-noise simulation plot 2200. The reference sampling PLL 1718attenuates the phase-noise plot 2208 coming from the primary VCO 1706 toquite an extent. This attenuation is clearly visible in the phase-noisesimulation plot 2200.

The other primary contributor in the phase-noise simulation plot 2200 isthe phase-noise coming from the reference clock TCXO 1724 present in thefirst embodiment of the present disclosure. A phase-noise plot 2210 isthe contribution of the TCXO 1724 into the reference sampling PLL 1718.The phase-noise plot 2210 is titled as XTAL in the phase-noisesimulation plot 2200. This phase-noise plot 2210 is the contribution ofthe TCXO 1724 in the reference sampling PLL 1718, when the TCXO 1724 isgenerating input frequencies of 100 MHz

The reference sampling PLL 1718 forwards the generated samplingreference frequency of 9.4 GHz towards the down-convert mixer 1716. Thedown-convert mixer 1716 mixes this generated sampling referencefrequency of 9.4 GHz with the incoming frequencies of 9.5 GHz-9.625 GHzto generate an attenuated intermediate frequency of 0.1 GHz to 0.225GHz. This attenuation procedure itself reduces the phase-noisecontributions coming from the reference VCO 1720 and the TCXO 1724.

FIG. 23 illustrates a phase-noise simulation plot 2300 contributed by areference sampling PLL when a TCXO clock (or another reference clock)generates input frequencies of 250 MHz in accordance with the firstembodiment of the present disclosure.

The two-dimensional phase-noise simulation plot 2300 comprises of anordinate (vertical axis) disclosing Phase-noise (dBc/Hz) 2302 and oneabscissae (horizontal axis) disclosing Frequency (Hz) 2304. Thephase-noise simulation plot 2300 discloses the phase-noise contributedby the reference sampling PLL 1718 as disclosed in the first embodimentof the present disclosure in FIG. 17. It is evidently visible that thephase-noise simulation plot 2300 has multiple contributors. The two mostimportant contributors to phase-noise in the phase-noise simulation plot2300 are the reference VCO 1720 and the TCXO 1724 as discussed in FIG.17.

A phase-noise plot 2308 is the contribution of the reference VCO 1720 inthe phase-noise simulation plot 2300. The reference sampling PLL 1718attenuates the phase-noise plot 2308 coming from the primary VCO 1706 toquite an extent. This attenuation is clearly visible in the phase-noisesimulation plot 2300.

The other primary contributor in the phase-noise simulation plot 2300 isthe phase-noise coming from the TCXO 1724 present in the firstembodiment of the present invention. A phase-noise plot 2310 is thecontribution of the TCXO 1724 into the reference sampling PLL 1718. Thephase-noise plot 2310 is titled as XTAL in the phase-noise simulationplot 2300. This phase-noise plot 2310 is the contribution of the TCXO1724 in the reference sampling PLL 1718, when the TCXO 1724 isgenerating input frequencies of 250 MHz

The reference sampling PLL 1718 forwards the generated samplingreference frequency of 9.4 GHz towards the down-convert mixer 1716. Thedown-convert mixer 1716 mixes this generated sampling referencefrequency of 9.4 GHz with the incoming frequencies of 9.5 GHz-9.625 GHzto generate attenuated intermediate frequencies of 0.1 GHz to 0.225 GHz.This attenuation procedure itself reduces the phase-noise contributionscoming from the reference VCO 1720 and the TCXO 1724.

FIG. 24 illustrates a phase-noise simulation plot 2400 contributed bythe main PLL in accordance with the second embodiment of the presentdisclosure. The two-dimensional phase-noise simulation plot 2400comprises of an ordinate (vertical axis) disclosing Phase-noise (dBc/Hz)2402 and one abscissae (horizontal axis) disclosing Frequency (Hz) 2404.The phase-noise simulation plot 2400 discloses the phase-noisecontributed by the main PLL 1812 as disclosed in the second embodimentof the present disclosure in FIG. 18. The primary difference between thephase-noise simulation plot 2400 and the above plots of FIGS. 21, 22 and23 is that there is no DDS present in the second embodiment of thepresent disclosure. The most important contributor of phase-noise in thephase-noise simulation plot 2400 is the TCXO 1802 as discussed in FIG.18.

A phase-noise plot 2412 is the contribution of the TCXO 1802 into themain PLL 1810. The phase-noise plot 2412 is titled as XTAL in thephase-noise simulation plot 2400. Due to the absence of a DDS in thesecond embodiment of the present invention, a phase detector plot 2410becomes a major factor.

The main PLL 1812 forwards the primary VCO 1810 generated outputfrequencies of 9.5 GHz-9.625 GHz towards the down-convert mixer 1816.The down-convert mixer 1816 mixes incoming the primary VCO 1810generated output frequencies of 9.5 GHz-9.625 GHz with the samplingreference frequency of 9.4 GHz and generates attenuated intermediatefrequencies of 0.1 GHz to 0.225 GHz. This attenuation procedure itselfreduces the phase-noise contributions coming from the TCXO 1802.

FIG. 25 illustrates a phase-noise simulation plot 2500 contributed by areference sampling PLL having the TCXO clock generating inputfrequencies of 100 MHz in accordance with the second embodiment of thepresent disclosure. The two-dimensional phase-noise simulation plot 2500comprises of an ordinate (vertical axis) disclosing Phase-noise (dBc/Hz)2502 and one abscissae (horizontal axis) disclosing Frequency (Hz) 2504.The phase-noise simulation plot 2500 discloses the phase-noisecontributed by the reference sampling PLL 1818 as disclosed in thesecond embodiment of the present disclosure in FIG. 18.

The primary contributor to the phase-noise simulation plot 2500 is thephase-noise coming from the TCXO 1802 present in the second embodimentof the present disclosure. A phase-noise plot 2510 is the contributionof the TCXO 1802 into the reference sampling PLL 1818. The phase-noiseplot 2510 is titled as XTAL in the phase-noise simulation plot 2500.This phase-noise plot 2510 is the contribution of the TCXO 1802 in thereference sampling PLL 1818, when the TCXO 1802 is generating inputfrequencies of 100 MHz

The reference sampling PLL 1818 forwards the generated samplingreference frequency of 9.4 GHz towards the down-convert mixer 1716. Thedown-convert mixer 1816 mixes this generated sampling referencefrequency of 9.4 GHz with the incoming frequencies of 9.5 GHz-9.625 GHzto generate a attenuated intermediate frequencies of 0.1 GHz to 0.225GHz.

FIG. 26 illustrates a phase-noise simulation plot 2600 contributed by areference sampling PLL having the TCXO clock (or another referenceclock) generating input frequencies of 250 MHz in accordance with thesecond embodiment of the present disclosure.

The two-dimensional phase-noise simulation plot 2600 comprises of anordinate (vertical axis) disclosing Phase-noise (dBc/Hz) 2502 and oneabscissae (horizontal axis) disclosing Frequency (Hz) 2504. Thephase-noise simulation plot 2600 discloses the phase-noise contributedby the reference sampling PLL 1818 as disclosed in the second embodimentof the present disclosure in FIG. 18.

The primary contributor to the phase-noise simulation plot 2600 is thephase-noise coming from the TCXO 1802 present in the second embodimentof the present disclosure. A phase-noise plot 2610 is the contributionof the TCXO 1802 into the reference sampling PLL 1818. The phase-noiseplot 2610 is titled as XTAL in the phase-noise simulation plot 2600.This phase-noise plot 2610 is the contribution of the TCXO 1802 in thereference sampling PLL 1818, when the TCXO 1802 is generating inputfrequencies of 250 MHz.

The reference sampling PLL 1818 forwards the generated samplingreference frequency of 9.4 GHz towards the down-convert mixer 1716. Thedown-convert mixer 1816 mixes this generated sampling referencefrequency of 9.4 GHz with the incoming frequencies of 9.5 GHz-9.625 GHzto generate a attenuated intermediate frequencies of 0.1 GHz to 0.225GHz.

FIG. 27 illustrates a flow chart 2700 depicting the operational methodsof the first embodiment in accordance with the present disclosure. Atstep 2702, the Reference Sampling PLL receives clock signals from aTCXO, generates sampling frequencies to eliminate digital noise floorand forwards the sampling frequencies towards a Down Convert Mixer.

At step 2704, the Main PLL receives clock signals from a low noisefrequency generator DDS, generates the output frequencies and forwardsthem towards the Down Convert Mixer.

At step 2706, the Down Convert Mixer which is a part of the Main PLLreceives frequencies coming from both the Main PLL and the ReferenceSampling PLL, mixes them to reduce a multiplication number N to achievehigh data rate, high modulation schemes and low phase deviation errors.

FIG. 28 illustrates a flow chart 2800 depicting the operational methodsof the second embodiment in accordance with the present disclosure. Atstep 2802, the Reference Sampling PLL receives clock signals from a TCXO(or another reference clock), generates sampling frequencies toeliminate digital noise floor and forwards the sampling frequenciestowards a Down Convert Mixer.

At step 2804, the Main PLL receives clock signals from the same TCXO,generates the output frequencies and forwards them towards the DownConvert Mixer.

At step 2806, the Down Convert Mixer which is a part of the Main PLLreceives frequencies coming from both the Main PLL and the ReferenceSampling PLL, mixes them to reduce a multiplication number N to achievehigh data rate, high modulation schemes and low phase deviation errors.

FIG. 29 illustrates a flow chart 2900 depicting the operational methodsof the third embodiment in accordance with the present disclosure. Atstep 2902, a TCXO generates a clock signal of the low noise of onefrequency that can be anywhere between 100 MHz to 250 MHz

At step 2904, a Sampling Phase Detector receives the clock signals andeliminates digital noise floor.

At step 2906, a Digital PLL is added with the Sampling PLL to improveperformance and reliability of an Ultra-Low Phase-noise FrequencySynthesizer to achieve high data rates, high modulation schemes and lowphase deviation errors.

In one embodiment of the invention the ultra-low phase noise frequencysynthesizer comprises at least one sampling Phase Locked Loop (PLL),wherein the at least one sampling PLL comprises:

-   -   a. at least one reference clock configured to generate at least        one first clock signal of at least one first clock frequency;    -   b. at least one sampling phase detector configured to receive        the at least one first clock signal and a single reference        frequency to generate at least one first analog control voltage;    -   c. a Digital Phase/Frequency detector configured to receive the        at least one first clock signal and a single reference frequency        to generate at least a second analog control voltage; and    -   d. at least one reference Voltage Controlled Oscillator (VCO)        configured to receive the at least one analog control voltage to        generate the single reference frequency;

Further, in one embodiment of the invention the ultra-low phase noisefrequency synthesizer configured to determine phase noise and quality ofat least one sampling Phase Locked Loop (PLL), wherein the at least onesampling PLL comprises:

-   -   a. at least one reference clock configured to generate at least        one first clock signal of at least one first clock frequency;    -   b. at least one sampling phase detector configured to receive        the at least one first clock signal and a single reference        frequency to generate at least one first analog control voltage;    -   c. a Digital Phase/Frequency detector configured to receive the        at least one first clock signal and a single reference frequency        to generate at least a second analog control voltage; and    -   d. at least one reference Voltage Controlled Oscillator (VCO)        configured to receive the at least one analog control voltage to        generate the single reference frequency;        Furthermore, in one embodiment of the invention A system for        detecting the surrounding environment of a vehicle comprising at        least first sensor, configured to obtain data, the at least        first sensor comprising a transmitter for transmitting at least        one radio signal to the one or more objects within the        surrounding environment. Further, the first sensor may include a        receiver for receiving the at least one radio signal returned        from the one or more objects. Furthermore, the first sensor may        include at least one ultra-low phase noise frequency synthesizer        configured to determine phase noise and quality of the        transmitted and the received at least one radio signal, wherein        the at least one ultra-low phase noise frequency synthesizer        comprises at least one sampling Phase Locked Loop (PLL), at        least one main PLL. Further, the system may include a processing        unit. The processing unit coupled to the at least first sensor        configured to gather electro-magnetic information about the one        or more objects. Further, the processing unit may classify or        recognize each of the one or more objects by analyzing the data,        wherein the classification or recognition is based on a unique        signature obtained from each of the one or more objects.        Furthermore, the processing unit may generate an electromagnetic        map of the surrounding environment by utilizing unique        signatures of the one or more objects. Again further, the        processing unit may combine the electromagnetic map with a        geographical map or physical map.

In one embodiment of the invention ultra-low phase noise frequencysynthesizer comprises at least one sampling Phase Locked Loop (PLL) andat least one main PLL. The at least one main PLL comprises at least oneFractional-N synthesizer, wherein the at least one Fractional-Nsynthesizer comprises:

-   -   a. at least one high frequency Digital Phase/Frequency detector        configured to receive and compare the at least one clock        frequency and at least one feedback frequency to generate the at        least one second analog control voltage and at least one digital        control voltage; and    -   b. at least one variable frequency divider configured to divide        at least one intermediate signal by a predetermined factor N to        generate at least one feedback signal of the at least one        feedback frequency.

Yet, in another embodiment of the invention the system for detecting thesurrounding environment of a vehicle comprising:

-   -   at least first sensor, configured to obtain data, the at least        first sensor comprising    -   A) a transmitter for transmitting at least one radio signal to        the one or more objects within the surrounding environment;    -   B) a receiver for receiving the at least one radio signal        returned from the one or more objects;    -   C) at least one ultra-low phase noise frequency synthesizer        configured to determine phase noise and quality of the        transmitted and the received at least one radio signal, wherein        the at least one ultra-low phase noise frequency synthesizer        comprises:    -   1) at least one sampling Phase Locked Loop (PLL), wherein the at        least one sampling PLL comprises:    -   a. at least one reference clock configured to generate at least        one first clock signal of at least one first clock frequency;    -   b. at least one sampling phase detector configured to receive        the at least one first clock signal and a single reference        frequency to generate at least one first analog control voltage;    -   c. a Digital Phase/Frequency detector configured to receive the        at least one first clock signal and a single reference frequency        to generate at least a second analog control voltage; and    -   d. at least one reference Voltage Controlled Oscillator (VCO)        configured to receive the at least one analog control voltage to        generate the single reference frequency;    -   2) at least one main PLL, wherein the at least one main PLL        comprises:    -   a. at least one first fixed frequency divider configured to        receive the at least one reference frequency and to divide the        at least one reference frequency by a first predefined factor to        generate at least one Direct Digital Synthesizer (DDS) clock        signal;    -   b. at least one high frequency DDS configured to receive the at        least one DDS clock signal and to generate at least one second        clock signal of at least one second clock frequency;    -   c. at least one high frequency Digital Phase/Frequency detector        configured to receive and compare the at least one second clock        frequency and at least one feedback frequency to generate at        least one second analog control voltage and at least one digital        control voltage;    -   d. at least one main VCO configured to receive the at least one        first analog control voltage or the at least one second analog        control voltage and generate at least one output signal of at        least one output frequency, wherein the at least one digital        control voltage controls which of the at least one first analog        control voltage or the at least one second analog control        voltage is received by the at least one main VCO;    -   e. at least one down convert mixer configured to mix the at        least one output frequency and the reference frequency to        generate at least one intermediate frequency; and    -   f. at least one second fixed frequency divider configured to        receive and divide the at least one intermediate frequency by a        second predefined factor to generate the at least one feedback        frequency;    -   3) a processing unit, coupled to the at least first sensor        configured to;    -   a. gather, electro-magnetic information about the one or more        objects;    -   b. classify or recognize each of the one or more objects by        analyzing the data, wherein the classification or recognition is        based on a unique signature obtained from each of the one or        more objects;    -   c. generate an electromagnetic map of the surrounding        environment by utilizing unique signatures of the one or more        objects; and    -   d. combine the electromagnetic map with a geographical map or        physical map.

In an embodiment of the invention the RADAR sensor transmits, receives,and process radio frequency signals that detects data includesinformation about the electromagnetic properties and characteristics ofthe objects of interest or the surroundings of the vehicle. The data mayinclude shape, silhouette, doppler or micro doppler information. Thedata may include depth, dimensions, direction, height, distance andplacement of the object of interest with respect to the vehicle.

The signal processing or the computation of such information and datamay include detecting and classification the type of the one or moreobjects includes living or non-living thing, stationary or movingobject, animal or human, standing or mobile human, metal, wood, orconcrete.

In one embodiment of the invention a system for detecting thesurrounding environment of a vehicle comprising: AA) at least firstsensor, configured to obtain data, the at least first sensor comprising:

-   -   A) a transmitter for transmitting at least one radio signal to        the one or more objects within the surrounding environment;    -   B) a receiver for receiving the at least one radio signal        returned from the one or more objects;    -   C) at least one ultra-low phase noise frequency synthesizer        configured to determine phase noise and quality of the        transmitted and the received at least one radio signal, wherein        the at least one ultra-low phase noise frequency synthesizer        comprises:        -   1) at least one sampling Phase Locked Loop (PLL), wherein            the at least one sampling PLL comprises:            -   a. at least one reference clock configured to generate                at least one first clock signal of at least one first                clock frequency;            -   b. at least one sampling phase detector configured to                receive the at least one first clock signal and a single                reference frequency to generate at least one first                analog control voltage;            -   c. a Digital Phase/Frequency detector configured to                receive the at least one first clock signal and a single                reference frequency to generate at least a second analog                control voltage; and            -   d. at least one reference Voltage Controlled Oscillator                (VCO) configured to receive the at least one analog                control voltage to generate the single reference                frequency;        -   2) at least one main PLL, wherein the at least one main PLL            comprises:            -   a. at least one Fractional-N synthesizer, wherein the at                least one Fractional-N synthesizer comprises:                -   i. at least one high frequency Digital                    Phase/Frequency detector configured to receive and                    compare the at least one clock frequency and at                    least one feedback frequency to generate the at                    least one second analog control voltage and at least                    one digital control voltage; and                -   ii. at least one variable frequency divider                    configured to divide at least one intermediate                    signal by a predetermined factor N to generate at                    least one feedback signal of the at least one                    feedback frequency;                    BB) a processing unit, coupled to the at least first                    sensor configured to:    -   a. gather, electro-magnetic information about the one or more        objects;    -   b. classify or recognize each of the one or more objects by        analyzing the data, wherein the classification or recognition is        based on a unique signature obtained from each of the one or        more objects;    -   c. generate an electromagnetic map of the surrounding        environment by utilizing unique signatures of the one or more        objects; and    -   d. combine the electromagnetic map with a geographical map or        physical map.

In one embodiment of the invention a system for detecting thesurrounding environment of a vehicle comprising at least first sensor.The first sensor may be configured to obtain data. The at least firstsensor comprising:

-   -   A) a transmitter for transmitting at least one radio signal to        the one or more objects within the surrounding environment;    -   B) a receiver for receiving the at least one radio signal        returned from the one or more objects;    -   C) at least one ultra-low phase noise frequency synthesizer        configured to determine phase noise and quality of the        transmitted and the received at least one radio signal, wherein        the at least one ultra-low phase noise frequency synthesizer        comprises:        -   1) at least one sampling Phase Locked Loop (PLL), wherein            the at least one sampling PLL comprises:            -   a. at least one reference clock configured to generate                at least one first clock signal of at least one first                clock frequency;            -   b. at least one sampling phase detector configured to                receive the at least one first clock signal and a single                reference frequency to generate at least one first                analog control voltage;            -   c. a Digital Phase/Frequency detector configured to                receive the at least one first clock signal and a single                reference frequency to generate at least a second analog                control voltage; and            -   d. at least one reference Voltage Controlled Oscillator                (VCO) configured to receive the at least one analog                control voltage to generate the single reference                frequency.                Further, the system for detecting the surrounding                environment of the vehicle comprises a processing unit,                coupled to the at least first sensor configured to;    -   a. gather, electro-magnetic information about the one or more        objects;    -   b. classify or recognize each of the one or more objects by        analyzing the data, wherein the classification or recognition is        based on a unique signature obtained from each of the one or        more objects;    -   c. generate an electromagnetic map of the surrounding        environment by utilizing unique signatures of the one or more        objects; and    -   d. combine the electromagnetic map with a geographical map or        physical map.

Yet, in another embodiment of the invention a method for detecting thesurrounding environment of a vehicle comprising:

AA) at least first sensor, configured to obtain a data, the at leastfirst sensor comprising

-   -   A) a transmitter for transmitting at least one radio signal to        the one or more objects within the surrounding environment;    -   B) a receiver for receiving the at least one radio signal        returned from the one or more objects;    -   C) at least one ultra-low phase noise frequency synthesizer        configured to determine phase noise and quality of the        transmitted and the received at least one radio signal, wherein        the at least one ultra-low phase noise frequency synthesizer        comprises:        -   1) at least one sampling Phase Locked Loop (PLL), wherein            the at least one sampling PLL comprises:            -   a. at least one reference clock configured to generate                at least one first clock signal of at least one first                clock frequency;            -   b. at least one sampling phase detector configured to                receive the at least one first clock signal and a single                reference frequency to generate at least one first                analog control voltage;            -   c. a Digital Phase/Frequency detector configured to                receive the at least one first clock signal and a single                reference frequency to generate at least a second analog                control voltage; and            -   d. at least one reference Voltage Controlled Oscillator                (VCO) configured to receive the at least one analog                control voltage to generate the single reference                frequency;        -   2) at least one main PLL, wherein the at least one main PLL            comprises:            -   a. at least one first fixed frequency divider configured                to receive the at least one reference frequency and to                divide the at least one reference frequency by a first                predefined factor to generate at least one Direct                Digital Synthesizer (DDS) clock signal;            -   b. at least one high frequency DDS configured to receive                the at least one DDS clock signal and to generate at                least one second clock signal of at least one second                clock frequency;            -   c. at least one high frequency Digital Phase/Frequency                detector configured to receive and compare the at least                one second clock frequency and at least one feedback                frequency to generate at least one second analog control                voltage and at least one digital control voltage;            -   d. at least one main VCO configured to receive the at                least one first analog control voltage or the at least                one second analog control voltage and generate at least                one output signal of at least one output frequency,                wherein the at least one digital control voltage                controls which of the at least one first analog control                voltage or the at least one second analog control                voltage is received by the at least one main VCO;            -   e. at least one down convert mixer configured to mix the                at least one output frequency and the reference                frequency to generate at least one intermediate                frequency; and            -   f. at least one second fixed frequency divider                configured to receive and divide the at least one                intermediate frequency by a second predefined factor to                generate the at least one feedback frequency;                BB) a processing unit, coupled to the at least first                sensor configured to:    -   a. gathering, electro-magnetic information about the one or more        objects;    -   b. classifying or recognize each of the one or more objects by        analyzing the data, wherein the classification or recognition is        based on a unique signature obtained from each of the one or        more objects;    -   c. generating an electromagnetic map of the surrounding        environment by utilizing unique signatures of the one or more        objects; and    -   d. combining the electromagnetic map with a geographical map or        physical map.

In one embodiment of the invention discloses a method of furthercomprising recognizing the object on interest after classifying, whereinthe classification of the object of interest includes living ornon-living thing, stationary or moving object, animal or human, standingor mobile human, metallic, wooden, or concrete objects.

FIG. 30A illustrates a “dead zone” within immediate surroundings of aLiDAR.

FIG. 30B illustrates a LiDAR mounted over the top of a vehicle, in orderto eliminate the “dead zone” as much as possible. These limits theoptions of using parking garages, causes difficulty in the use ofrooftop accessories and finally also makes the vehicle less marketablesince such a tower does not blend in well with the design of a vehicle.

FIG. 31 illustrates non-reliability of a LiDAR in adverse weather suchas rain, fog, and snow. LiDAR systems use mechanical rotation mechanismsthat are prone to failure.

Typical LiDAR systems rely on a rotation speed of around 5-15 Hz. Thismeans that if a vehicle moves at a speed of 65 mph, the distance thevehicle travels between “looks” is about 10 ft. RADAR sensor systems areable to continuously scan their surroundings especially when thesesystems use one transmitting and one receiving antenna (Bistatic system)(as depicted in FIG. 32A). Further, LiDAR systems are not accurate indetermining the speed and autonomous vehicles rely on RADAR for accuratespeed detection.

FIG. 32B illustrates the effect of phase-noise in the distance andangular resolution of the RADAR. The clutter to noise ratio (CNR) in astandard RADAR, as shown in the figure, restricts the longer distanceresolution and the angular resolution. This results innon-identification of smaller objects present on the road. Also, the CNRrestricts the range of the RADAR that may be distance or angular range.

FIG. 32C illustrates resolution problem with RADAR signals. The RADARsignals, due to high phase-noise are not able to distinguish betweenclosely placed objects and slow-moving objects nor can the object beimaged correctly, or its electrical characteristics determined withsufficient reliability. Due to the high phase-noise, vehicles may nothave a clear picture of what is in-front or surrounding them. RADARsystems may give wrong and/or error-prone readings. This happens due tothe lesser resolution characteristics. Phase-noise plays a crucial rolein the resolution which has been determined clearly in the prior art.Due to this low-resolution characteristic, the objects close to thevehicle and slowly moving are not properly distinguished. As seen, inthe figure, the correct position of the vehicle in-front may not beaccurately ascertained and may give blurry or multiple appearances.Also, as shown in the figure, very closely placed objects like twopeople close by may be read as a single object. Hence, for closeobjects, there is no differentiation.

FIG. 33 depicts usage of available light to determine the surroundingsof an autonomous vehicle.

Further, in old-fashioned Radars, the Doppler effect gets a little morecomplicated since a Radar is sending out a signal and expects to areceived signal that is lower in power but at the same frequency when ithits an object. If this object is moving, then this received signal willbe subject to the Doppler effect and in reality, the received signalwill not be received at the same frequency as the frequency of thetransmit signal. The challenge here is that these frequency errors canbe very subtle and could be obscured by the phase-noise of the system(as shown in FIG. 34). The obvious drawback is that vital informationabout the velocity of an object gets lost only because of phase-noise(see figure below). The above is especially right when dealing withobjects that move slower than airplanes and missiles, such as cars,bicycles, pedestrians, etc.

Further, the spectral picture of a processed signal looks like the FIG.35. As one can see the spectral picture contains also unwanted sidelobes. One major contributor to the side lobes is the phase-noise of theRadar system. This spectral regrowth of side lobes can cause errors inthe determination of the actual distance of a target and can obscure asmall target that is close to a larger target. It can also cause errorsin target velocity estimation. As shown in FIG. 36, weaker returnsignals can get obscured in the side lobes of a stronger signal.

FIG. 37 illustrates a detection system 3700, in accordance with variousembodiments of the present invention. The detection system includes, butnot limited to, a RADAR unit 3702, an ultra-lowphase-noise frequencysynthesizer 3704, a memory 3706 and a specialized processor 3708. TheRADAR unit 3702 may be configured for detecting the presence of one ormore objects/targets in one or more directions by transmitting radiosignals in the one or more directions. In an embodiment, the RADAR unit3702 may be comprised of at least one of or combination of: atraditional RADAR system, a synthetic aperture RADAR, or a type of RADARwith a number of RADAR subsystems and antennas. Further, the RADAR unitmay include a transmitter for transmitting at least one radio signalwith the goal of receiving a received signal; from one or more objects.

The ultra-lowphase-noise frequency synthesizer 3704 (hereinafter mayinterchangeably be referred to as ‘frequency synthesizer 3704’) may beproviding the needed carrier frequency, or a multiple or a fraction ofthe needed carrier (or up-conversion) frequency for the RADARtransmitter section. The ultra-low noise frequency synthesizer 3704 mayalso be providing the needed carrier (or down-conversion) frequency, ora multiple or a fraction of the needed carrier frequency for the RADARreceiver section. For some RADARs the transmit signal is used as thecarrier frequency for the receive section, this does not alter theprinciple of this invention and is merely another form ofimplementation. The term “carrier” may be used interchangeably referringto an up-conversion or down-conversion signal.

The output of the frequency synthesizer 3704 may include a continuouswave signal with ultra-lowphase-noise. The received radio signal may bedown-converted with the help of frequency synthesizer 3704 and analyzedto determine information and/or characteristics corresponding to the oneor more objects. Such information may include (but not limited to)velocity, distance, frequency and type(s) of the object(s). The distancemay be determined between the object and the RADAR unit 3702.Accordingly, an action may be adopted by the system 3700 based on theinformation received from the analysis of the synthesized radio signal.For example, if the detection system 3700 is implemented in a vehicle,the RADAR unit 3702 may transmit the radio signals, that in this casewill carry ultra-low or negligible phase-noise and following that thereceived return signal will carry ultra-low or negligible phase-noise aswell. The return signal will be analyzed to determine the presence ofthe objects and their locations with respect to the vehicle. The factthat an ultra-low phase-noise synthesizer is used practically guaranteesthat the information corresponding to the one or more objects may bedetermined in a more accurate fashion than for traditional RADAR systemsand thus, accordingly, a correct decision may be taken based onexecution of the instructions set by the processor 3708.

Further, in case of a RADAR unit, the received signal (i.e., receivedfrom hitting an object and returning as echo) will be subject to theDoppler effect. A system implemented in autonomous vehicles that utilizethe characteristics of the Doppler effect, potential slow-moving targetswill create a very small Doppler frequency shift that is very hard toimpossible to identify with traditional RADAR systems that do notimplement an ultra-low noise synthesizer. As a result of themuch-improved phase-noise at low-offset frequencies from the carrier,such signals have a significantly better chance to be detected andanalyzed correctly. Further, the traditional method of analysis does notneed to change and should also not change the amount of data produced bythe analysis, it will, however—improve the accuracy of the informationcreated by the RADAR system.

The memory 3706 may include instructions set 3710 having one or morespecialized instructions and a database 3712. The specializedinstructions may be executable by the specialized processor 3708. Thedetection system 3700 may receive an input from an external source (notshown). Such input may be provided to activate the detection system3700. The input may be a command that may be provided through an inputsource such as a keyboard or a switch. Further, the input may beprovided remotely to the detection system 3700 that may be processed bythe processor 3708 in accordance with the instructions set 3710. In anembodiment, once the input is received by the system 3700, thetransmitter of the RADAR unit 3702 may initiate transmitting the radiowaves in multiple directions. For example, the radio waves may betransmitted everywhere in 360 degrees to determine the presence of oneor more objects.

In an embodiment, the RADAR unit 3702 of the detection system 3700 maybe placed above the vehicle, or anywhere else in or on the vehicle, totransmit the radio signals in various directions and further to receivethe returned radio signals as returned from the object(s) (after hittingthe object(s)).

Further, the specialized instruction on execution by the specializedprocessor gathers information corresponding to the one or more objectsand surroundings corresponding thereto, the information being gatheredbased on the returned radio signal. Specifically, the informationcorresponding to the object may be gathered from the radio signal thatis returned from the object(s). The information gathered may include,but is not limited to, surrounding information of the object(s) such asroad structure, traffic in front of those objects, traffic behavior, theresponse of the one or more objects based on the traffic behavior.Further, based on analysis of these, the system may determine probablenext move corresponding to the one or more objects and performpre-planning corresponding to probable moves that may be required innear future. Such intelligent planning of the detection system maydetermine probable events on the road. This further ensures safety onthe road. Further, the information corresponding to the objects andsurrounding may be stored in the database 3712 for future reference andplanning. Furthermore, the information may be provided to an outputdevice (not shown) such as a display. The information may also betransferred to a Database outside of the autonomous vehicle such as, butnot limited to, a cloud-based database.

With the implementation of the ultra-low phase-noise frequencysynthesizers 3704, the improved phase noise translated to degrees isbetter than 0.04 degrees. The detection system is further explained withan illustration in conjunction with the following figure(s) (i.e., FIG.38).

FIG. 38 illustrates an exemplary vehicle 3800 implementing a detectionsystem (such as the detection system 3700), in accordance with anembodiment of the present invention. The RADAR unit 3802 of thedetection system may be configured anywhere inside or outside thevehicle 3800. For example, as depicted, the RADAR unit 3802 is placed onthe top of the vehicle to detect the presence of one or more objects inone or more directions. Such objects may include, but is not limited to,other vehicles, human, animal, flying objects or other types of objectsin the surroundings. Further, the objects may be of varying size andtype and may have different characteristics that may be determined bythe RADAR unit. Herein, the characteristics of objects may include, butnot limited to, shape, size, running speed, maximum possible speed (forexample, based on the type of the vehicle) and so on. In an embodiment,the RADAR unit 3802 may be positioned at another place such as outsideor inside the vehicle.

Further, the RADAR unit 3802 may include a transmitter for transmittingradio signals created in conjunction with an implementation of anultra-low phase-noise synthesizer. This signal is transmitted with thegoal to be echoed back (returned radio signal) from one or more objects(that may be available in the surrounding or other near areas). Further,the RADAR unit 3802 may include a receiver for receiving the returnedradio signal generated by its paired transmitter or another transmitterfrom the one or more objects. As depicted, signals 3804 may betransmitted and received by the RADAR unit 3802.

In an embodiment, the ultra-lowphase-noise frequency synthesizer(hereinafter may interchangeably be referred to as ‘frequencysynthesizer 3704’) may be configured inside the vehicle 3800 that may becoupled to the radio unit 3802 positioned on outside surface of thevehicle. In an alternate embodiment, the frequency synthesizer 3704 maybe configured on outside surface of the vehicle along with the RADARunit.

Further, the detection system may include a processor for processing ofthe refined radio signals that may further be analyzed to determineinformation corresponding to the one or more objects. Further, theprocessor may determine additional information corresponding to the oneor more objects based on the determined information and one or morefactors. Such information and additional information may include (but isnot limited to) velocity, distance, angular position, frequency andtype(s) of the object(s). The distance may be determined between theobject and the RADAR unit 3702 and accordingly the expected time bywhich the object may reach near the vehicle and vice versa may also bedetermined. Herein, the one or more factors may include (but not limitedto) at least one of one or more input instructions, analysis of pasthistory, and current situation.

Further, a suitable action may be determined by the detection system3700 (for example, by the processor thereof) based on analysis of therefined radio signal. Such determined suitable action may then beperformed by the vehicle 3800. For example, the processor (as shown inFIG. 37) may provide signals for actuation of one or more components ofthe vehicle to perform one or more actions based on the one or moreinput instructions. In an embodiment, the detection system 3700 mayinclude an input unit for receiving one or more input instructions froman external source (such as a user or device). Such instructions may bestored in a memory (as shown in FIG. 37) of the detection system.

In an embodiment, the objects, such as other vehicles, may alsoimplement the detection system. Further, through the RADAR unit, eachobject (such as, but not limited to the vehicle) may be able tointeract/interact/communicate with other objects through RADAR signalseither by utilizing RADAR signals generated by other objects or anotherform of interaction. Furthermore, the detection system may enabledetermining location and more characteristics of other objects (such asa vehicle) having the detection system therein.

In an embodiment, the detection system may further be equipped with oneor more components and/or sensors to enable additional functioningcorresponding thereto. For example, LiDAR, cameras, image processing,and additional sensors may be utilized to create a 3D mapping of thesurroundings Further, the object such as a vehicle (having the detectionsystem) may receive instructions from an external source that may beprocessed effectively by implementing the detection system. For example,if the vehicle (having the detection system) receives instructions toreach a particular place at a remote location. The detection system incombination with GPS may enable the vehicle to reach to such particularplace. In an embodiment, the detection system may further utilize one ormore cameras that may provide additional information regarding theenvironment (and the objects therein) to the detection system.Advantageously, this enables self-driving of the vehicle withoutexternal guidance from a person (such as a driver).

In another embodiment of the invention, the vehicle 3800 may have morethan one RADAR units placed on it. Each RADAR unit may have a separatetransmitting and receiving antenna. Inputs from various RADAR units maybe accumulated and processed by the processor (to be explained later inthe detailed description) to generate a 3-dimensional map of the roadtraversed by the vehicle 3800. Inputs may also be taken from variousother sensors to determine the correctness of data received.

To facilitate object detection, RADAR 3702 may provide information aboutthe vehicle's (3800) environment by generating and transmitting to theenvironment RADAR signals and measuring the returned signals. Acomputing device like processor 3708 or system associated may thendetermine the distances and relative locations of objects using the dataprovided by the RADAR unit 3702.

More specifically, to detect an object based on range data received froma RADAR unit 3702 and/or other sensors, a vehicle's computing system maygenerate a 3D point cloud based on information captured by the sensors.The RADAR unit 3702 and/or another sensor may capture information aboutobjects in the form of data points, which may make up the 3D pointcloud. The information associated with the various data points may alsobe referred to as range data and may provide information related to theposition and orientation of objects in the environment relative to thevehicle (e.g., a 3D mapping of the environment). The data points mayalso enable a computing system to determine materials and otherinformation about objects in the environment.

A point cloud is a set of data points in some coordinate system. Datapoints within a point cloud may be organized within a 3D coordinatesystem, which may be defined by an X, Y, and Z coordinates, for example.In addition to providing information relating to distances betweenobjects and the RADAR unit 3702, the data points may also represent theexternal surface of an object or multiple objects. Within a point cloud,a computing system may cluster similar points together and fill in theappropriate patterns according to the data points to detect objectswithin the environment. The computing system may use various techniquesto convert a point cloud to a 3D surface. Some approaches, like Delaunaytriangulation, alpha shapes, and ball pivoting, build a network oftriangles over the existing vertices of the point cloud, while otherapproaches convert the point cloud into a volumetric distance field andreconstruct the implicit surface so defined through a marching cubesalgorithm.

Furthermore, the processor 3708 may analyze the data points within the3D point cloud (as data points or as determined 3D surfaces) todetermine the sizes, positions, and/or orientations of objects in theenvironment. Likewise, the computing device recognizes objects within apoint cloud by clustering data points that correspond to the same objectand may also determine the materials of the object based on thereflections of the data points on the various objects.

However, using clustering techniques to identify specific objects withininformation formatted into a 3D point cloud may require substantialamounts of time and/or processing power. Further, an object of interestmay not be identified using clustering techniques due to the layoutand/or positioning of the object within the environment. For example, acomputing system may not be able to detect a pedestrian standing infront of a tree or vehicle within a 3D point cloud because pointclustering may cluster and identify the pedestrian and the other objectas a single object, which thus has an unknown shape. The processor 3708may need to perform multiple iterations of analysis to separate andidentify the pedestrian from the other object during the 3D point cloudanalysis. Thus, while formatting RADAR data in a 3D point cloud mayallow a computing device to identify objects in some instances, thecomputing device may need to devote more resources (e.g., time/power) toaccurately scan and identify objects within the cloud. The exampleimplementations discussed may help to overcome some of the deficienciesthat can arise during the execution of traditional clusteringtechniques.

For instance, in an example embodiment, a vehicle's computing system maygenerate a spherical data set based on the range data gathered from theRADAR unit 3702 or other vehicle sensors. Generating the spherical dataset may involve the processor projecting the RADAR return signal intospherical coordinates or some other useful format. A sphericalcoordinate system may be represented as a coordinate system forthree-dimensional space, where the position of a point is specified bythree numbers: the radial distance of the point from a fixed origin, itspolar angle measured from a fixed zenith direction, and the azimuthangle of its orthogonal projection on a reference plane that passesthrough the origin and is orthogonal to the zenith, measured from afixed reference direction on that plane. Other coordinate-based systemsmay be used as well.

FIG. 39 illustrates RADAR 3702 and its various components, in accordancewith an embodiment of the invention. The RADAR 3702 includes asynchronizer 37022, a power supply 37024, a transmitter 37026, areceiver 37030, an RF Power divider 37028, and an antenna 37032.

The synchronizer 37022, synchronizes signal generation for the RADAR3702. The synchronizer 37022, may include a clock to perform thisfunctionality. This helps the RADAR 3702 to generate signals pulses atequal intervals of time maintaining the intervals of the time constant.The specific function of the synchronizer is to produce TRIGGER PULSESthat start the transmitter, indicator sweep circuits, and rangingcircuits.

Power supply 37024, provides power to various components within theRADAR unit 3702.

The RF Power Divider 37028, provides the TX signal to the receiver,where it may be used as Local Oscillator for down-conversion to abaseband signal. When a single antenna is used for both transmission andreception, as in most monostatic radar systems, the Power divider 37028must be used. In high power Radar systems, the Power divider may beimplemented as an Isolator or RF coupler to protect the receiver fromthe high power output of the transmitter. In low power RADAR systems, aPower divider may be sufficient.

The transmitter 37026, performs various important functions. Thefunctions include the creation of radio waves to be transmitted,conditions the wave to form the pulse train, and amplifies the signal toa high-power level to provide adequate range.

The receiver 37030, receives the signals when returned by hitting theobjects. The receiver 37028 detect wanted echoes in the received signalin the presence of noise, clutter, and interference; and amplifies thedesired signals for subsequent processing.

The generated signals are generated and returned signals are receivedusing the antenna 37032. The transmitter 37026 and the receiver 37030may use a single antenna. Such a construction is known as monostaticRADAR unit. However, in other embodiment, the transmitter 37026 and thereceiver 37030 may utilize different antennae. In such RADAR units,Power divider 37028 may be eliminated. Such a construction is known asbi-static RADAR unit. This concept is not limited to only onetransmitter and one receiver, multiple transmitters and multiplereceivers may be utilized to create a multi-static Radar unit. Thenumber of transmitters and receivers do not have to be necessary equal.

FIG. 40A is a line diagram depicting an exemplary 3-dimensional mapgeneration using monostatic RADARs, in accordance with an embodiment ofthe invention. In this, the vehicle 3800 may be provided with multiplemonostatic RADAR units 4002. In a preferred embodiment, at least onemonostatic RADAR unit may be placed on the front part, a rear part, leftthe side, right side, and top of the vehicle 3800. There may also beother places like A and B pillars of the vehicle 3800 on which thesemonostatic RADAR units may be placed. Signals transmitted from one ofthese monostatic RADAR units, when reflected by bouncing off fromobjects may be received by rest of the other monostatic RADAR units andvice versa. Information so obtained by all the RADAR units may becombined to form a 3-dimensional mapping of the surrounding. The3-dimensional map may be generated by a processor (not shown in thefigure) communicably connected to the multiple RADAR units.

FIG. 40B is a line diagram depicting an exemplary 3-dimensional mapgeneration using bistatic RADARs, in accordance with an embodiment ofthe invention. In this embodiment, there may be a single bi-static RADARunit 4004 placed on the vehicle 3800. The bi-static RADAR unit may havea transmitting unit 40044 and receiver unit 40042 placed at differentlocations of the vehicle 3800. For example, the transmitting unit 40044may be placed in front of the vehicle 3800 whereas the correspondingreceiving unit 40042 may be placed at the rear-end of the vehicle 3800.This configuration may allow an increase in aperture size and provide ahigher resolution of data. Data acquired using the bi-static RADAR unitmay be processed to generate a 3-dimensional mapping of the surrounding.Hence, generating a clearer view of all the objects in the near vicinityof the vehicle 3800.

In yet another embodiment there may be multiple bi-static RADAR unitsplaced all over the vehicle with the corresponding transmitting andreceiving unit placed at different locations. Transmitted signals fromtransmitting unit of one bi-static or multi-static RADAR unit whenbounces off objects and get returned may be received by receiving unitsof all bi-static or multi-static RADAR units present on the vehicle3800. Data thus captured may be then utilized to create ahigh-resolution 3-dimensional mapping of the surrounding region.

FIG. 41 is a line diagram illustrating an exemplary 3-dimensional mapgeneration using RADAR units present on different vehicles, inaccordance with an embodiment of the invention. In this configuration,there may be present multiple vehicles 4102, 4104, and 4106 having theircorresponding RADAR units 4108, 4110, and 4112. Each of the RADAR units4108, 4110, and 4112 may include their own corresponding processor (notshown in the figure). All these processors may be connected to eachother wirelessly through cloud computing, mobile communication networksor connected car technology. As per the embodiment, the RADAR unit 4108when transmits signals to identify object 4114, the RADAR signalsreturned due to bouncing off from the object 4114, are received not onlyby the RADAR unit 4108 but, by all the close vicinity RADAR units like4110 and 4112 as well and vice versa. The processors of all the RADARunits 4108, 4110, and 4112 identify which all RADAR units are nearby orwithin a close proximity. The proximity may be defined or automaticallyadapted by the processor. When close proximal RADAR units areidentified, each processor communicates with another processor to joinand form an identification mode in which the returned RADAR signals fromevery object are received from every vehicle. The data received may thenbe combined in real time to generate a 3-dimensional mapping of thesurroundings.

FIG. 42 illustrates an exemplary method 4200 flow diagram for anautonomous vehicle, in accordance with an embodiment of the presentinvention. The method 3900 corresponds to functional steps forimplementation of an object detection system, such as the detectionsystem 3700. The order in which method is performed is not construed aslimiting for the present invention. Further, various additional stepsmay be added in light of the scope of the present invention.

The method 4200 may include various steps such as (but are not limitedto): at step 4202, the method may detect a presence of one or moreobjects in one or more directions by a RADAR unit. Herein, the RADARunit comprising: a transmitter for transmitting at least one radiosignal to the one or more objects; and a receiver for receiving the atleast one radio signal returned from the one or more objects. Further,the method may include, utilizing (step 4204) at least one ultra-lowphase-noise frequency synthesizer for refining the transmitted and thereceived signals of the RADAR unit, and thereby determining aphase-noise and maintaining the quality of the transmitted and thereceived radio signals.

Herein, the method may further include various steps such as receivingand multiplying, by the ultra-low phase-noise frequency synthesizer, theat least one output signal by a predefined factor to generate at leastone final output signal of at least one final output frequency. Further,the method may generate the up converting or down converting the signalof the RADAR unit, by utilizing the ultra-low phase-noise frequencysynthesizer.

The method 4200 (at step 4206) may process the returned (received)signals that are refined (such as down converted) by the ultra-lowphase-noise frequency synthesizer to determine one or morecharacteristics of the one or more objects (from where the RADAR signalsare returned). For example, the method may determine the presence of aslow-moving target despite the very small Doppler frequency shift.Further, the method may include determining the presence of aclose-range target despite the very short signal travel time.Furthermore, the method may determine a distance and a direction of eachof the one or more objects. Again further, the method may determine atype of material an object is made up of, its geometric structure,orientation, surface characteristics and electrical characteristics.Additionally, the method may include a step of creating a RADAR imageand/or activating one or more additional sensors for operation thereofin conjunction and synergy with the RADAR unit. The method may determinecharacteristics of two close objects irrespective of the size of theobjects. Further, the method may differentiate between two or more typesof the objects when one object is visually obscuring another object.

Based on the gathered information (such as the characteristics)corresponding to the objects, the method (at step 4208) may enable anautonomous vehicle to adopt one or more actions based on the determinedcharacteristics of the objects. In an embodiment, such actions may besuggested by the specialized processor to the autonomous vehicle (orcomponents thereof).

Advantageously, the present invention emphasizes that by incorporatingthe ultra-low phase-noise synthesizer in an existing RADAR system, theperformance of the RADAR system will be improved substantially in termsof target detection accuracy and resolution and because of this it canbecome the dominant sensor for the handling of autonomous cars. Herein,the Synthesizer drastically reduces the phase-noise of RADAR signals sothat such RADAR sensor will be able to replace current sensor systems atvery low cost and with reliability at all lighting and adverse weatherconditions.

Further, the RADAR unit may utilize modulated or unmodulated waveformsto determine the electromagnetic characteristics such as, but notlimited to, dielectric constants of targets with significantly betteraccuracy. Furthermore, for the RADAR unit (as disclosed herein thisdisclosure) that may utilize modulated or unmodulated waveforms, theprocessing speed of the main decision-making unit is significantlyimproved because the much lower phase-noise enables the RADAR to providemore accurate data with less data than a LiDAR sensor for example.Accordingly, the detection system (such as the detection system 3700)may determine the type of material of a target object with highaccuracy. Furthermore, the detection system may utilize one or more of:a camera, image processing and additional low-cost sensors for replacingthe need for LADAR/LiDAR. Moreover, in an embodiment of the presentdisclosure, the RADAR unit (that may use modulated or unmodulatedwaveforms) may be utilized in conjunction with LiDAR, Camera, imageprocessing used to create a 3D mapping of the surroundings.Additionally, the RADAR unit (that may use modulated or unmodulatedwaveforms) may be used in SAR applications or other RADAR application.Herein, when used in bistatic or multistatic scenarios the phasecorrelation between the separate transmitters and receivers may be keptbetween 10 to 1000 times better than with traditional RADAR systems.Further, the detection unit may determine probable events of objects(such as other vehicles in traffic area) through RADAR unit and byreducing the phase-noise through the phase-noise frequency synthesizer.Accordingly, the detection unit may pre-plan event based on thedetermination of probable events and may ensure safety on the road,especially during traffic time.

In an embodiment, the vehicle 3800 may include a detection system (suchas the detection system 3700) having a RADAR unit (such as the RADARunit 3802) that may utilize a non-modulated pulse to determine thepresence of a slow-moving target despite the very small Dopplerfrequency shift. Further, the RADAR unit utilizes a non-modulated pulseto determine the presence of a close-range target despite the very shorttravel time of the signal. Further, in an embodiment, the RADAR unitutilizes at least one of: a modulated waveform and/or a non-modulatedwaveform to determine at least one of: the distance and morecharacteristics of a target, one or more characteristics of two closetargets when both are the same size or one target is smaller than theother target. Further, the detection system determines a type ofmaterial a target is made up of.

Further, in an embodiment, the RADAR unit utilizes modulated orunmodulated waveforms wherein when used in conjunction with targets thatutilize RADAR signal modulators can identify the modulation. Further,the detection system may utilize modulated or unmodulated waveforms inRADAR unit in conjunction with at least one of: Cameras, imageprocessing and additional low-cost sensors to create a 3D mapping of thesurroundings. Furthermore, a RADAR system that utilizes an ultra-lowphase-noise synthesizer may be used as an imaging RADAR that candiscover silhouettes and create a true 3-dimensional map of thesurroundings of the vehicle including the mapping of the object that isnot visible with light

FIG. 43 illustrates a flowchart depicting 3-dimensional map generationmethod 4300, in accordance with an embodiment of the invention. Theorder in which method is performed is not construed as limiting for thepresent invention. Further, various additional steps may be added inlight of the scope of the present invention.

Method 4300 may include various steps such as (but are not limited to);at step 4302, the method may identify the presence of one or more RADARunits present nearby. The identification of the RADAR units may be basedon a continuous ID signal being emitted by each of the RADAR units andproximity distance is within a threshold limit or automatically fixed bya processor of the RADAR unit 3700. The method 4300 (at step 4304) maythen initiate a communication through a wireless network, withcorresponding processors all of the RADAR units identified. The wirelessnetworks may be mobile networks like 4G, 3G, CDMA, etc. or cloudnetwork, or even connected car's network.

Further (at step 4306), the processor may then initiate anidentification mode switching that configures the nearby RADAR units toreceive the signals returned after being bounced off from differentobjects. All such data is then collected by each of the processors ofthe RADAR units and then shared with each other as well through thewireless network. The data is then combined to generate a 3-dimensionalmapping of the surrounding of the vehicle 3800 and also other proximalvehicles.

Further in an embodiment, the combined data may be uploaded by each ofthe proximal vehicles to a central database. The combined data may begeotagged by combining GPS information. This data is then stored in thecentral database with an exact model of the street of up to a distanceof 1 cm. The central database may contain a repository of such streetinformation and may keep updating it in real time whenever, newinformation from data collected may be identified. Hence, whenever, anew vehicle, that has never traversed the street is scheduled to crossthe street, this information may be sent to the new vehicle forautonomous driving purposes. Hence, the new vehicle may forecastexpected road situations, traffic, stop signs, traffic lights etc.

FIG. 44 illustrates a line diagram depicting RADAR signals from theRADAR system as presented in accordance with the present invention.There is an increase in RADAR resolution, in contrast to the resolutionas depicted in FIG. 32B, due to the usage of the Ultra-Low Phase-noiseFrequency Synthesizer. The Ultra-Low Phase-noise Frequency Synthesizeris responsible for an improved CNR. The improvement in CNR results inimproved angular resolution accuracy of the RADAR and hence, RADAR isable to identify a small object present on the road and also within aclose vicinity as well.

FIG. 45 is a line diagram illustrating object identification, inaccordance with an embodiment of the present invention. As opposed tothe earlier identification problems, due to the increased resolution ofRADAR signals, the RADAR signals are able to accurately identify theposition of slow moving objects. With the improved CNR, the RADARsignals are also able to identify closely placed objects. In this, ascan be seen, the RADAR signals, due to ultra-low phase caused due to thepresence of the ultra-low phase-noise frequency synthesizer providesclear data and distinction between various objects whether obscured byother objects, slowly moving or having very close distance between eachother.

Therefore, according to the present invention, the RADAR system,including the Ultra-Low Phase-noise Frequency Synthesizer, may be ableto work even in adverse weather conditions. High accuracy is achievedusing such RADAR system. High phase-noise that leads to high CNR isdecreased to provide an accurate reading. Due to the decreasedphase-noise, cluttered, blurred or multiple appearances of slow-movingobjects or closely placed objects may be eliminated. The decreased CNRratio also improves the angular resolution of the RADAR to identifyclosely present small objects. The RADAR system of the present inventionmay also be able to identify material composition of objects presentwithin its vicinity. Hence, effectively distinguishing between humansand wooden poles. The Ultra-Low Phase-noise Frequency Synthesizerpresent within the RADAR may improve phase-noise by more than 20 dBc/Hz.

FIG. 46A illustrates a line diagram depicting identification of obscuredobjects by the RADAR unit 3702, in accordance with an embodiment of theinvention. The vehicle 3800 includes the RADAR unit 3702. The RADAR unit3702 is able to generate RADAR signals that may identify visuallyobscured objects. Hence, the RADAR unit 3702, with the help of theultra-lowphase-noise synthesizer 3704, may be able to identify a human4604, behind a tree 4602. The ultra-lowphase-noise synthesizer 3704helps by providing better resolution of the data. The amplitude of thetransmitted and returned RADAR signals may be calculated to identify theobjects that may be visually obscured. Further, electromagneticcharacteristics may include permittivity, permeability, relativepermeability, diamagnetism, paramagnetism among others. Electromagneticcharacteristic levels of the surface of objects may be utilized toidentify the type of objects in correlation with the density of theobjects. In another exemplary environment, the detection system 3700,may be able to identify a human sitting in a bus stop obscured by aposter on the bus stop. The electromagnetic characteristics principlemay be utilized to identify such obscured objects.

FIG. 46B, illustrates a method 4650 to identify visually obscuredobjects, in accordance with an embodiment of the invention. The method4650 is initiated at step 4652, wherein the detection system 3700initiates transmission of RADAR signals to identify objects surroundingand gain knowledge of the environmental surroundings. Further at step4654, the returned signals that are bounced of the objects on whichRADAR signals transmitted fall upon.

The method 4650 further at step 4656, classifies the objects identifiedat step 4654. Objects may be classified based on the dimensions of theobjects. The object may be small, medium or large. If the object is notlarge the method 4650 jumps to step 4664 that will be explained later inthe description. If the object identifier is classified as a largeobject, at step 4658, then further at step 4660, the method 4650determines whether the object is a stationary object or not. suchclassification of objects may be performed utilizing comparison ofsubsequent images pixel-wise. Images may be partitioned into a multipleof pixel blocks that may be compared with pixel blocks of subsequentimages in short time intervals. A difference observed may be used toclassify the object as mobile and not stationary. If the object isclassified as not stationary, the method 4650 jumps to step 4666 detailsof which will be explained later. If the object is classified asstationary, the method 4650 jumps to step 4662.

The method 4650, at step 4662, focuses the RADAR signals to the objectidentified. This is done as the object may be a potential threat to thevehicle 3800. This may be because there may be some obscured objectbehind the object determined that may pose a threat to the vehicle 3800.Further, the method 4650 (at step 4664) determines whether there is anobscured object behind the object and the status of the obscured objects(stationary or mobile). This may be done by a variety of method. In onemethod, electromagnetic characteristics of the objects may be identifiedby studying the returned RADAR signals. In another method, the densityof the material of the objects may be utilized and matched to templates,stored, to classify the obscured objects. Also, the status of theobscured objects is identified whether they are stationary or mobile.This may be performed using the pixel-wise comparison of the returnedradar signals.

At step 4666, the method 4650, marks the obscured object identified as apoint of interest. The point of interest is an object classified to keepa track of. The priority of keeping the track of such an object is highas it may be a potential threat. Hence, the detection system 3700,continuously monitors all such point of interest objects.

By way of example, the detection and imaging unit 3700 identifieswhether there is a bus box in near vicinity or not. Further, in casethere is a bus box identified, the detection and imaging unit 3700further determines an obscured object and whether the obscured object isa potential threat or not. Like there may be an object in a poster andit might have a human figure. The RADAR 3702 is capable enough toidentify whether it's a poster or a human figure. The RADAR thus mayalso identify what is behind the trunk of a tree. It is able todetermine a deer or other animal that may cause a potential threat tothe driver within the vehicle 3800.

FIG. 47, illustrates a line diagram depicting identification of pavementand road, in accordance with an embodiment of the invention. The vehicle3800 while traveling the road 4702 has to identify where exactly roadends and where exactly pavement starts.

Roadway materials, especially asphalt, are heterogeneous mixtures ofair, rocks, and binder. Each of the roadway materials have particularelectromagnetic characteristics associated with its ability to bepolarized by an electric field. The electromagnetic characteristics maybe linearly related to the polarizability and may be a complex quantity.The electromagnetic characteristics may be complex having real, andimaginary components, representing energy storage and energy lossrespectively to a propagating electromagnetic wave. Typically, whenspeaking of the dielectric constant one is referring to the real part ofthe electromagnetic characteristics which can be frequency dependentdepending on the frequencies of interest.

The detection system 3700, is capable of identifying clearly theblacktop road surface based on the dielectric constant of the roadwaymaterial. The density of the roadway material may be recognized withaccuracy using the detection system 3700. Due to this property, an exactimage of the blacktop of the road may be recognized. It may be easy todistinguish the recognized road with dirt mud etc.

Further, it may be easy to recognize the pavement 4704. The detectionsystem 3700 is capable of identification of pavements as pavements areelevated at about 15 cm from the road 4702. The detection system 3700 iscapable of identifying the continued elevated pavement edge. Thedetection system 3700 is configured to accurately measure the elevatededge of the pavement 4704 with high-resolution signal reception.

Further the detection system 3700 is able to collect information aboutthe surroundings for mapping purposes. Every item made out of a metalcan be mapped accurately. Also, electromagnetic information aboutbuildings, electrical poles, traffic lights, traffic sign, vegetation,below ground features and other environmental feature can be collectedto create an “electromagnetic-map”. Such an electromagnetic map willprovide an additional layer of mapping to LiDAR and visual mapping.Electromagnetic maps will provide another layer of mapping that has notexisted before and looks the same under all conditions.

In one embodiment, mapping the static metal object in the city and thespecific signature of each object provide additional information on theelectromagnetic-map of the city. For example, traffic lights metal polesmay have different electromagnetic information and signature, that canbe mapped into an electromagnetic-map of the city. In a similar way,metal, glass, concrete or wooden buildings may have differentelectromagnetic information and signature that can be mapped intoelectromagnetic-map of the city. Different buildings and objects mayhave different sizes, shapes, structure and build from combinations ofdifferent material. The different buildings may have differentelectromagnetic information and signatures that can be mapped intoelectromagnetic-maps. Multiple layers of these maps can be correlated togeographical maps, physical maps, visual maps, GIS maps and another typeof maps.

An electronic signature of an object can be correlated to theelectromagnetic-map, in a similar way that a visual image can becorrelated to a “visual map” or a LiDAR-image can be correlated to aLiDAR-map. Different layers provide a different type of information forthe electromagnetic information, visual information, and LiDARinformation that may be synthesized into the relevant layer. Fusion ofthese information layers and the relevant map layers provide additionalinformation about the objects.

Further, in another exemplary usage, the detection of human beings ispossible by using the different reflection of the RADAR signals. Sincethe reflected signal is of high resolution from the RADAR 3702, due tothe ultra-low phase-noise therefore, it is possible to distinguisheasily between various materials including humans, animals, liveobjects, trees, plants, concrete, glass, wall, etc. Different Radarreflections may also be achieved by utilizing different frequencies andmultiple chirp and radar signal modulation types with the goal ofdetermining the electromagnetic characteristics so that the material ofand object of interest can be derived.

Further, in another exemplary usage, the detection system 3700, may beable to identify animate and non-animate objects using the sameprinciple of electromagnetic characteristics as discussed above. Suchusage helps the detection system 3700 to identify where are humans orlive objects are present on the road. Detection of humans is based onthe fact that there may be some movement always due to breathing ormovement of body parts (as in case of a walking person) etc. This smallmovement may be used to detect a human being from other objects behind awall or bus stops. The ultra-lowphase-noise synthesizer 3704 is able toreduce the high clutter from the wall and other objects nearby toprovide a clear resolution data. Furthermore, the RADAR 3702 is alsoable to identify and classify humans behind walls made up of differentmaterials from a far-off distance as well.

In another embodiment of the invention, the RADAR 3702 may also be ableto identify human being making a minimum of movements like waiving oftheir hands etc. even when they are blocked behind an opaque object. Theultra-low phase-noise synthesizer 3704 is able to reduce the clutternoise and able to do the identification. Even in conditions when theweather is adverse, like raining, snowing, darkness etc. The RADAR 3702may detect humans from a considerable amount of distance. This may helpto do away with the collision warning systems in the autonomous vehiclesas they might not be required. This is so because due to very earlywarning times, the vehicle may be able to reroute their trajectorywithout being coming to a state of emergency.

Detection of human beings may be done analyzing the Fourier spectrogramof Doppler frequency. Doppler signatures for a moving object varies andthe speed and distance of the movement play a role as well. also h,there might be other objects like other animals or cars due to which theDoppler signature may vary. Hence, small sections of the frequencyspectrum may be analyzed individually. Since the RADAR 3702 is ahigh-resolution RADAR, it is capable of providing much clearer data asto slow-moving objects whether near or far with an emphasis on humanbeings.

Humans may also be detected using a background subtraction technique.This technique helps in detection of obscured human beings behinddifferent kinds of object. In this technique, successive frames of thecross-correlation signals between each received signal and thetransmitted signal. This technique can help in identification of movingobjects, especially humans clearly in a heavily cluttered environment aswell.

The RADAR 3702, uses a micro-Doppler effect to identify humans. Themicro-Doppler effect relies on low amplitude signals that make uphuman's Doppler Signature. These micro-Doppler signals can also beutilized to identify stationary humans by determining heartbeat from thereflected signals. Thus, in an environment full of people, due to themicro-Doppler technique and high-resolution property of the RADAR 3702,the imaging system 3700 is able to identify, based on gait, differentmobile humans and stationary humans due to the breathing patterns andthe heartbeat patterns.

In addition to the above, the imaging system 3700, using the RADAR 3702may also be utilized to count the number of human beings after theiridentification. With the ultra-low noise capability of the RADAR 3702,real-time counting of the human beings can be done. Even in highlycluttered regions, the RADAR 3702 is able to count the number of humanbeings efficiently.

The high-resolution power of the RADAR 3702, as per the invention, helpsthe autonomous vehicle to reconstruct its surrounding environment. Thereconstruction helps in gathering details just like a human being wouldand that too in a 3-D environment. The RADAR 3702, may be complementedwith an artificial engine that helps in beamforming and beam steering.Use of beamforming techniques helps the artificial engine to identifyand classify various types of objects within the surroundings. Theartificial engine may also be able to track and recognize all sorts ofobjects and hence further help the autonomous vehicle in its moreautonomous functioning.

This high-resolution ability of the RADAR 3702 provides it superhumanlike capabilities like for example seeing through the objects. Combinewith a highly efficient and adaptive artificial engine, the autonomousvehicle is not only able to identify and classify objects behind largeobjects but also able to predict whether they object poses a threat tothe autonomous vehicle or not. The pure radio signals help in clearimaging of the objects and the RADAR 3702 misses fewer objects. TheRADAR 3702, with a highly directive RF beam, may be able to identify andaccurately determine location, and speed of road objects and that toounder severe weather conditions and even in disarranged environments.

In another embodiment of the invention, due to the ultra-low phase-noiseproperty of the RADAR 3702, it is also able to identify tiny objects aswell. So, an object that may have been undetected with a RADAR havingnormal phase-noise skirt, may now be detected using the RADAR 3702 dueto ultra-low phase-noise skirt.

Such a RADAR 3702, when working in tandem with other sensors like LiDAR,Ultrasound, camera, and other RADAR sensors may prove to be very usefuland may be able to contribute more and accurate information for thesensory fusion data. combined with accurate data from RADAR 3702, andLiDAR, camera, and other sensor data, the imaging system 3700 may beable to provide an accurate 3-D map of the objects surrounding thevehicle.

As for the close placed object, generally due to clutter noise, theRADARs are not capable of distinguishing and classify so close placedobjects. RADAR 3702, due to the ultra-low phase-noise is able to clearlydetermine and classify the objects within close proximity of each other.

FIG. 48, illustrates a block diagram of the processor 3708 of thedetection system 3700 and its various internal components. Thecomponents of the processor 3708 may be hardware or software based.Further, the processor 3708 may include multiple microprocessors toperform various different functions that may be coordinated with eachother to perform a coordinated output.

The processor 3708 may include a data acquisition module 4804, a dataclassification module 4806, a data comparison module 4808, a3-dimensional map generation module 4810, and the identification of anobject module 4812. The processor 3708 may be configured to collect datafrom various other sensors like 4802A-4802C (cumulatively referred to assensors 4802) and also from RADAR 3702. The various other sensors may beLiDAR, camera, ultrasound sensors etc.

The data acquisition module 4804 receives data from every sensorconnected to the processor 3708. Data received is forwarded to the dataclassification module 4806. The data classification module 4806segregates what data is received from which sensor. This helps inidentification of data and its source sensor. The data is then forwardedto a data comparison module 4808. The data comparison module 4808compares data, similar in information, from all sensors. The comparisonis performed to check the data authenticity and helps in ascertainingthe correctness of collected data. Further, the compared data is thenforwarded to a 3-dimensional map generation module 4810, that correlatedthe data from all the sensors and generates a 3-dimensional mapping ofthe surroundings of the vehicle 3800. Further, the objectsidentification module 4812, identifies the animate and inanimate objectsutilizing continuously collecting data and comparing it with thehistorical data by breaking the data into pixels. Hence, the variousmodules working in a coordinated manner are able to produce a3-dimensional mapping and accurately identify the stationary and mobileobjects. Hence providing enough information to the vehicle 3800 to takean appropriate action during an event. In an exemplary embodiment, thesensors 4802 may be utilized to gather more information after beingidentified preliminarily. For e.g. the RADAR 3702 may preliminarilyidentify that there is a visually obscured object behind a tree etc.Then one of the sensors from the sensors 4802 like the LiDAR sensor maybe focused on that specific spot identified by the RADAR 3702. Then theLiDAR may use its high-resolution capability to identify clearly theobject. Further, in another exemplary usage, the RADAR 3702 may identifythat there is a live object in environment ahead of the vehicle 3800.The imaging and detection system 3702 may then initiate the LiDAR sensorto determine more details about the live object. Hence, instead offocusing the LiDAR all around 360 degrees, it may be efficiently used asa supporting sensor and may be focused to a smaller degree of the arealike 2-3 degrees and resolve objects better when preliminarilyidentified or determined by the RADAR 3702. It may be understood thatthere might be another sensor that may also be used in place of LiDARsensor or in conjunction with it like the camera to better understandthe specifics of the object.

FIG. 49, illustrates a flowchart of a method 4900 for identifying liveobjects using the detection system 3700, in accordance with anembodiment of the invention. Live objects have the tendency to move atall times. For e.g. a human being is moving all the time. Even if thehuman being is stationary, it is still breathing and hence, it may beidentified by capturing the chest cavity movement using appropriatefrequency RADAR signals.

The method 4900, (a step 4902), transmits RADAR signals to identifyobjects surrounding and gain knowledge of the environmentalsurroundings. The detection system 3700 may continuously vary thefrequency of transmitted signals. Various frequency signals aretransmitted in order to identify and obtain more information about theobjects preliminarily identified by the detection system 3700.

Once objects are identified the method, at step 4904, densityinformation of the objects is obtained. The density information of theobjects is determined by processing the returned RADAR signals. Further,at step 4906, the method 4900 determines whether the object is live ornot. The object, is determined to be not living, the method 4900 isterminated. However, in case the object is identified to be living, thedetection system 3700, marks the object as a point of interest object,at step 4908. As described earlier, the point of interest is an objectclassified to keep a track of. The priority of keeping the track of suchan object is high as it may be a potential threat. Hence, the detectionsystem 3700, continuously monitors all such point of interest objects.

By way of an example, the detection system 3700 identifies if there is alive object that may be obscured by some other bigger object. Thevehicle 3800, when driven in autonomous mode, needs to have informationabout every object within its vicinity especially the live objects likehumans. Such humans may be potential threats as they may come in frontof the vehicle 3800 suddenly and may cause an accident.

Advantageously, the present disclosure discloses usage of Radar withultra-low phase-noise technology in conjunction with SAR technology orwithout to be combined with LiDAR as an overall surroundings mappingsolution. Further, in an embodiment, the Radar with ultra-lowphase-noise technology with or without SAR technology (monostatic,bi-static or multi-static) may be utilized in combination with LiDARwhere the Radar provides an overall view and directs the LiDAR to pointsof interest. Furthermore, the Radar with ultra-low phase-noisetechnology in conjunction with or without SAR technology (monostatic,bi-static or multi-static) to be combined with a Camera/thermal camerato identify objects/silhouettes. Also, the Radar with ultra-lowphase-noise technology in conjunction with or without SAR technology(monostatic, bi-static or multi-static) may be utilized by combiningwith one or more other sensors.

Additionally, in an embodiment, the present invention discloses creatingsynergy between Radar and LiDAR by supporting and cross-referencingLiDAR imaging by providing information about an object surfaceroughness, Geometric structure, Orientation. Further, in an embodiment,SAR, InSAR, and PolSAR may be utilized to achieve the advantages.Furthermore, exchange/completion of data between vehicles—lowphase-noise ensures accurate knowledge about the distance between thevehicles which is crucial for such type of informationexchange/completion.

Hence, the RADAR unit 3702, according to a present embodiment of theinvention is able to identify moving human beings in severe weatherconditions may be dark, snow, rain, etc. the RADAR 3702 is able toidentify the humans behind stationary objects like a tree and able toidentify and classify the various objects. The humans are identifiedfrom a safe distance which may eliminate the need for a collisionwarning system as it will not be required. The RADAR 3702 may also beable to identify materials of the objects on the road like for examplebike material etc.

Examples of sensor fusion with better visual interpretation:

1. Pedestrian approaching a crosswalk:

The RADAR 3702 classifies the pedestrian based on material detection ofthe human living body. RADAR 3702 further classifies the pedestrianbased on micro doppler of the limb movement. After detection andclassification, by the RADAR 3702, from a long range, it alerts theLiDAR and the Camera sensors to focus on the specific location andclarify the detection and classification.Pedestrian is further classified by the LiDAR on basis of shape; andFurther, the cameras as well classify the pedestrian. All the identifiedinformation is then fed into the processing unit that forms a decisionto stop for the pedestrian to cross.2. A cyclist on the road:The RADAR 3702 classifies the cyclist based on material detection of thehuman living body. The RADAR 3702 classifies the cyclist based onmaterial detection of cycle metal from a very long range. RADAR 3702further classifies the cyclist based on sinusoidal micro doppler of thepedal movement.After detection and classification, by the RADAR 3702, from a longrange, it alerts the LiDAR and the Camera sensors to focus on thespecific location and clarify the detection and classification.A cyclist is further classified by the LiDAR on basis of shape; andThe cyclist, the cameras as well classifies the pedestrian.All the identified information is then fed into the processing unit thatforms a decision to increase the distance from the cyclist.3. A deer or other animal on the side of the road:The RADAR 3702, due to the high resolution gained from the ultra-lowphase-noise signal in the captured information has the ability to detectand determine a deer from a very long range, even in adverse weatherconditions such as n fog, rain, snow or even in darkness. The RADAR 3702is also able to detect the presence of the deer even when the deer isobscured behind another object like a tree, bushes, or a wooden wall.

FIG. 50, illustrates a block diagram of a detection system 5000 fordetecting surrounding environment, in accordance with an embodiment ofthe invention. The system 5000 is similar to the detection system 3700as discussed in conjunction with FIG. 37.

The system 5000 includes, but not limited to a first sensor or the RADARunit 3702, as has been described above in conjunction with FIG. 39, theultra-low phase-noise frequency synthesizer 3704, a processor 5004, atleast one-second sensor 5002, and a database 5006.

The RADAR unit 3702 may be configured for detecting the presence of oneor more objects/targets in one or more directions by transmitting radiosignals in the one or more directions. In an embodiment, the RADAR unit3702 may be comprised of at least one of or combination of: atraditional RADAR system, a synthetic aperture RADAR, or different typesof RADAR with different RADAR subsystems and antennas.

The at least one-second sensor 5002 may be a LiDAR, that emits lightperiodically to gather data about the surroundings. The function of theLiDAR has been discussed above in detail.

The ultra-low phase-noise frequency synthesizer 3704 (hereinafter mayinterchangeably be referred to as ‘frequency synthesizer 3704’) may beproviding the needed carrier frequency, or a multiple or a fraction ofthe needed carrier (or up-conversion) frequency for the RADARtransmitter section. The ultra-low noise frequency synthesizer 3704 mayalso be providing the needed carrier (or down-conversion) frequency, ora multiple or a fraction of the needed carrier frequency for the RADARreceiver section. For some RADARs the transmit signal is used as thecarrier frequency for the receive section, this does not alter theprinciple of this invention and is merely another form ofimplementation. The term “carrier” may be used interchangeably referringto an up-conversion or down-conversion signal.

The database 5006 may include instructions set having one or morespecialized instructions. The specialized instructions may be executableby the processor 5004.

In an embodiment, the RADAR unit 3702 of the system 5000 may be placedin-front of the vehicle 3800, or anywhere else in or on the vehicle3800, to transmit the radio signals in various directions and further toreceive the returned radio signals as returned from the object(s) (afterhitting the object(s)).

Further, the specialized instruction on execution by the processor 5004gathers information corresponding to the surroundings, the informationis gathered based on the returned radio signal. The information gatheredmay include, but is not limited to, surrounding information of theobject(s) such as road structure, traffic in front of those objects,traffic behavior, the response of the one or more objects based on thetraffic behavior. Further, based on analysis of these, the system 5000may mark the one or more objects identified as an object of interest.The object of interest may be a potential threat to the vehicle 3800.The processor 5004 decides and performs pre-planning corresponding toprobable moves that may be required in near future. Such intelligentplanning of the system 5000 may determine probable events on the road.This further ensures safety on the road. Further, the informationcorresponding to the objects and surrounding may be stored in thedatabase 5006 for future reference and planning. Furthermore, theinformation may be provided to an output device (not shown) such as adisplay. The information may also be transferred to a Database outsideof the autonomous vehicle such as, but not limited to, a cloud-baseddatabase.

FIG. 51, illustrates an exemplary method 5100 flow diagram for detectingand imaging objects for a vehicle, in accordance with an embodiment ofthe invention. The method 5100 corresponds to functional steps forimplementation of an object detection system, such as the detectionsystem 5000. The order in which method is performed is not construed aslimiting for the present invention. Further, various additional stepsmay be added in light of the scope of the present invention.

The method 5100 may include various steps such as mentioned below, but,not limited to the ones as described. The method 5100, starts at stepS102 wherein the method 5102 scans the surrounding environment. Herein,the RADAR unit 3702 comprising: a transmitter for transmitting at leastone radio signal to one or more objects in the surrounding; and areceiver for receiving the at least one radio signal returned from theone or more objects in the surrounding. The method 5102 may include,generating (at step S104) a preliminary data of the surroundingenvironment. The preliminary data may include various small and large,stationary and mobile object information. Preliminary data may identifypreliminary details about surrounding environment for example, whereexactly are the objects present at what approximate direction anddistance. Further, the method 5100 determines (at step S106), if thereis an object that may be marked as an object of interest. The object ofinterest may be an object that may pose a potential threat to thevehicle 3800. This object may be an animal behind a tree, a child behinda vehicle a vehicle that has been put to ignition at the very moment ofscanning, a person sitting in a bus station, etc. Further, the method5100 initiates (at step S108) the at least second sensor 5002. The atleast second sensor may be, as described earlier, a LiDAR, a thermalcamera, or an ultrasound sensor. Such initiation helps in targeting themore resolution power of the second sensor 5002 to be targetedselectively to such part of the surrounding environment that may presenta potential threat to the vehicle 3800. The at least second sensor 5002,obtain (at step S110) supporting information about the object ofinterest. The supporting information may be information about size,edges, outline, actual classification of the object etc. Once thesupporting information has been gathered, the method 5100 combines (atstep S112) preliminary and supporting information about the surroundingenvironment. The method 5100 generates (at step S114) a 3-dimensionalimage of the surrounding image in order to provide a holistic and nearnatural image to the vehicle 3800.

The first sensor 3702 that is the RADAR unit 3702 and the at leastone-second sensor may obtain data using different techniques. The secondsensor may be equipped to obtain or provide data with more resolutionand up to a very close distance to the vehicle 3800.

FIG. 52, illustrates a block diagram of the processor 5004 and itsvarious internal components to perform detection of objects, accordingto an embodiment of the invention. The processor 5004 includes (but notlimited to) various modules as listed. The processor 5004 may include adata acquisition module 5052, a preliminary data module 5054, a sensorcontrol module 5056, a support data module 5058 and a 3-d data module5060.

The components of the processor 5004 may be hardware or software based.Further, the processor 5004 may include multiple microprocessors (ascomponents) to perform different functions that may be coordinated witheach other to perform a coordinated output.

The data acquisition module 5052 is configured to accept data from thefirst sensor 3702 and the at least one-second sensor 5002. The dataacquisition module 5052 translates the data from sensors 3702 and 5002to a format readable to the other parts of the processor 5004.

The sensor control module 5056 is configured to control the initiation,speed and data acquisition from the sensors 3702 and 5002.

The preliminary data from the first sensor or the RADAR unit 3702 isreceived by the preliminary data module 5054. The preliminary datamodule 5054 identifies if there is an object of interest that is presentin the data. in a situation, an object of interest is determined, thepreliminary data module 5054 forwards this information to the sensorcontrol module 5056. The sensor control module 5056, initiates the atleast one-second sensor 5002 to capture more data about the object ofinterest. By initiation, the sensor is basically activated that may bein off state before initiation or in a state wherein it may not becollecting data. However, in other embodiment of the invention, thesensor may be generally collecting information without focusing on aspecific area of surrounding or a specific object. Data acquired by theat least one-second sensor 5002 is then translated in a format that maybe appropriate to read by the support data module 5058. The support datamodule reads and generates more information about the object ofinterest. Both the preliminary data and the support data is alsoforwarded to the 3-dimensional data module to combine the two types ofdata and generate a holistic picture of the surrounding including allobjects, road, pavement, etc.

FIG. 53, illustrates a method 5300 flowchart for identification of apotential threat object to the vehicle 3800 using the detection system5000, in accordance with an embodiment of the invention. The method 5300may include additional or lesser steps. The steps presented here neednot be considered limiting to the invention.

The method 5300, at step S302, receives 2-dimensional data capturedusing the RADAR unit 3702. Further, at step S304, the RADAR data isprocessed for identifying the surrounding environment information andvarious objects in it. Further, at step S306, the object of interest isidentified by the method 5300. In an embodiment, there may be multipleobjects of interests identified by the RADAR unit 3702. This informationis used by the method 5300 to initiate, at step S308, LiDAR sensor 5002(the at least one-second sensor is interchangeably referred to as LiDARsensor 5002). The LiDAR sensor 5002 obtains, at step S310, multiple dataset pints of the object of interest. At step S312, the method identifiesinformation from the multiple dataset points like edges, curves, etc.Further, at step S314, the 2-dimensional data and the multiple data setpoints are combined and at step S316, a 3-dimensional image of theobject or objects of interest is generated.

FIG. 54, illustrates a block diagram displaying advantages of usingselective use of the at least second sensor, in accordance with anembodiment of the invention. As mentioned, the at least one secondsensor 5002. The RADAR unit 3702 may look all around the vehicle 3800 asrepresented by azimuth area 5402. So, the RADAR unit 3702 will sweep theentire azimuth area. Then in a scenario that the object of interest isidentified, the LiDAR 5002 may be directed to that particular area 5404.Such selective scanning helps to reduce the computing cycle of azimutharea 5402 by approximately 1/100^(th) of a general scenario when LiDARis sweeping the full azimuth itself. This happens because of the amountsof data reduced by the selective scanning use of the LiDAR 5002.

FIG. 55, illustrates a block diagram displaying advantages of usingselective use of the at least second sensor, in accordance with anembodiment of the invention. The RADAR 3702 may sweep an entireelevation region 5406 initially for preliminary data. In a scenario thatobject of interest is determined to be within the preliminary data, theLiDAR sensor 5002 is then directed to obtain more information about thesame area 5408 as shown in the figure. The selective scanning helps toreduce the computing cycle of azimuth area by approximately 1/10 of ageneral scenario when LiDAR is sweeping the full elevation 5406 itself.This happens because of the amounts of data reduced by the selectivescanning use of the LiDAR 5002.

Combining the azimuth area computing cycle saving and the elevation areacomputing cycle saving, the detection system 5000, as per the invention,is able to save 1/1000 computing cycles. Hence the amount of data to beprocessed is lowered by a minimum of 1/1000 of the data than from asituation when the full computation is performed by LiDAR itself.

Further, the redundant data also helps in providing a better datarepresentation and surety of the data obtained. It may also decrease theamount of computing power required as large amounts of data generated bya full servicing LiDAR is also reduced significantly.

FIG. 56 illustrates a method 5600, for recognition of a living body bythe RADAR 3702. The method 5600, (a step S602), transmits RADAR signalsto identify objects surrounding and gain knowledge of the environmentalsurroundings. The detection system 3700 may continuously vary thefrequency of transmitted signals. Various frequency signals aretransmitted in order to identify and obtain more information about theobjects preliminarily identified by the detection system 3700.

Once objects are identified by the method, at step S604, informationabout the electromagnetic characteristics of the material of the objectis obtained. The information of the type of the material of the objectsis determined by processing the returned RADAR signals. At step S605,additional Radar information of objects is obtained. This additionalRadar information may include information such as Doppler,micro-Doppler, velocity, size, shape, placement among other radarinformation types.

Further, at step S606, the method 5600 determines whether the object islive or not. If the object, is determined to be not human or livingbody, the method 5600 is terminated. However, in case the object isidentified to be living, the detection system 3700, marks the object asa point of interest object, at step S608. As described earlier, thepoint of interest is an object classified to keep a track of. Thepriority of keeping the track of such an object is high as it may be apotential threat. Hence, the detection system 3700, continuouslymonitors all such point of interest objects.

FIG. 57, illustrates an exemplary method 5700 flow diagram for detectingand imaging objects for a vehicle, in accordance with an embodiment ofthe invention. The method 5700 corresponds to functional steps forimplementation of an object detection system, such as the detectionsystem 3700. The order in which method is performed is not construed aslimiting for the present invention. Further, various additional stepsmay be added in light of the scope of the present invention.

The method 5700 may include various steps such as mentioned below, but,not limited to the ones as described. The method 5700, starts at stepS702 wherein the method 5702 scans the surrounding environment. Herein,the RADAR unit 3702 comprising: a transmitter for transmitting at leastone radio signal to one or more objects in the surrounding; and areceiver for receiving the at least one radio signal returned from theone or more objects in the surrounding. The method 5702 may include,generating (at step S704) a preliminary data of the surroundingenvironment. The preliminary data may include various small and large,stationary and mobile object information. Preliminary data may identifypreliminary details about surrounding environment for example, whereexactly are the objects present at what approximate direction anddistance. Further, the method 5700 determines (at step S706), if thereis an object that may be marked as an object of interest. The object ofinterest may be an object that may pose a potential threat to thevehicle 3800. This object may be an animal behind a tree, a child behinda vehicle a vehicle that has been put to ignition at the very moment ofscanning, a person sitting in a bus station, etc. Further, the method5700 initiates (at step S708) other sensors. The other sensors may be,as described earlier, a LiDAR, a thermal camera, or an ultrasoundsensor. Such initiation helps in targeting the more resolution power ofother sensors to be targeted selectively to such part of the surroundingenvironment that may present a potential threat to the vehicle 3800 andobtain more clear and redundant information about the object ofinterest. The other sensors, obtain (at step S710) supportinginformation about the object of interest. The supporting information maybe information about size, edges, outline, actual classification of theobject etc. Once the supporting information has been gathered, themethod 5700 combines (at step S712) preliminary and supportinginformation about the surrounding environment. The method 5700 generates(at step S714) a 3-dimensional image of the surrounding image in orderto provide a holistic and near natural image to the vehicle 3800.

FIG. 58, illustrates a block diagram of a detection system 5800 fordetecting surrounding environment, in accordance with an embodiment ofthe invention. The system 5800 is similar to the detection system 3700as discussed in conjunction with FIG. 37.

The system 5800 includes, but not limited to a first sensor or the RADARunit 3702, as has been described above in conjunction with FIG. 39, theultra-low phase-noise frequency synthesizer 3704, a processor 5804, atleast one-second sensor 5802, and a database 5806.

The RADAR unit 3702 may be configured for detecting the presence of oneor more objects/targets in one or more directions by transmitting radiosignals in the one or more directions. In an embodiment, the RADAR unit3702 may be comprised of at least one of or combination of: atraditional RADAR system, a synthetic aperture RADAR, or different typesof RADAR with different RADAR subsystems and antennas.

The at least one-second sensor 5800 may be a LiDAR, that emits lightperiodically to gather data about the surroundings. The function of theLiDAR has been discussed above in detail.

The ultra-low phase-noise frequency synthesizer 3704 (hereinafter mayinterchangeably be referred to as ‘frequency synthesizer 3704’) may beproviding the needed carrier frequency, or a multiple or a fraction ofthe needed carrier (or up-conversion) frequency for the RADARtransmitter section. The ultra-low noise frequency synthesizer 3704 mayalso be providing the needed carrier (or down-conversion) frequency, ora multiple or a fraction of the needed carrier frequency for the RADARreceiver section. For some RADARs the transmit signal is used as thecarrier frequency for the receive section, this does not alter theprinciple of this invention and is merely another form ofimplementation. The term “carrier” may be used interchangeably referringto an up-conversion or down-conversion signal.

The database 3706 may include instructions set having one or morespecialized instructions. The specialized instructions may be executableby the processor 5004.

In an embodiment, the RADAR unit 3702 of the system 5800 may be placedin front of the vehicle 3800, or anywhere else in or on the vehicle3800, to transmit the radio signals in various directions and further toreceive the returned radio signals as returned from the object(s) (afterhitting the object(s)).

Further, the specialized instruction on execution by the processor 5804gathers information corresponding to the surroundings, the informationis gathered based on the returned radio signal. The information gatheredmay include, but is not limited to, surrounding information of theobject(s) such as road structure, traffic in front of those objects,traffic behavior, the response of the one or more objects based on thetraffic behavior. Further, based on analysis of these, the system 5800may mark the one or more objects identified as an object of interest.The object of interest may be a potential threat to the vehicle 3800.The processor 5804 decides and performs pre-planning corresponding toprobable moves that may be required in near future. Such intelligentplanning of the system 5800 may determine probable events on the road.This further ensures safety on the road. Further, the informationcorresponding to the objects and surrounding may be stored in thedatabase 5806 for future reference and planning. Furthermore, theinformation may be provided to an output device (not shown) such as adisplay. The information may also be transferred to a Database outsideof the autonomous vehicle such as, but not limited to, a cloud-baseddatabase.

FIG. 59 illustrates a block diagram of the processor 5804 and itsvarious internal components to perform detection of objects, accordingto an embodiment of the invention. The processor 5804 includes (but notlimited to) various modules as listed. The processor 5804 may include adata acquisition module 5952, a preliminary data module 5954, a sensorcontrol module 5956, a support data module 5958 and a 3-d data module5960.

The components of the processor 5804 may be hardware or software based.Further, the processor 5804 may include multiple microprocessors (ascomponents) to perform different functions that may be coordinated witheach other to perform a coordinated output.

The data acquisition module 5952 is configured to accept data from thefirst sensor 3702 and the at least one-second sensor 5802. The dataacquisition module 5052 translates the data from sensors 3702 and 5802to a format readable to the other parts of the processor 5804.

The sensor control module 5956 is configured to control the initiation,speed and data acquisition from the sensors 3702 and 5802.

The preliminary data from the first sensor or the RADAR unit 3702 isreceived by the preliminary data module 5954. The preliminary datamodule 5954 identifies if the identified object is a human body bycomparing the electromagnetic characteristics of the surface of thehuman body present in the returned data. If the object identified isdetermined to be human, the preliminary data module 5954 marks it as anobject of interest and forwards this information to the sensor controlmodule 5956. The sensor control module 5956, initiates the at leastone-second sensor 5802 to capture more data about the object of interestand verify the data obtained by preliminary data module 5954. Byinitiation, the sensor is basically activated that may be in off statebefore initiation or in a state wherein it may not be collecting data.However, in other embodiment of the invention, the sensor may begenerally collecting information without focusing on a specific area ofsurrounding or a specific object. Data acquired by the at leastone-second sensor 5802 is then translated in a format that may beappropriate to read by the support data module 5958. The support datamodule reads and generates more information about the object ofinterest. Both the preliminary data and the support data is alsoforwarded to the 3-dimensional data module to combine, compare andverify the presence of a human body from the two types of data.

FIG. 60 illustrates a block diagram of system 6000, depicting formationof RADAR maps. The system 6000 includes the vehicle 3800 including RADAR3702 is able to identify various objects while the vehicle 3800 isdriving or in a mobile status. As stated above, RADAR 3702 includingultra-low phase-noise frequency synthesizer 3704, is able to identifyobjects that even cameras and LiDARs may not able to recognize. Theradio frequency spectrum of RADAR, LiDAR, and Camera are in differentspectrums and different characteristics, and different signal rangedetections and resolutions. They can detect objects in different waysbased on different return signals and signatures. RADAR 3702 is able totransmit radio frequency signals that collide various objects and getreturned. Electromagnetic characteristics of Radio frequency of theRADAR 3702 generate a unique type of information about objects presentaround the vehicles. This type of information is unique to RADAR and maynot visible to LiDAR and Camera.

RADAR 3702 thus identifies all stationary objects and also classifiesthe objects based on return signals. Since, RADAR 3702 is able toidentify metal objects from a very long distance hence, it can alsoclassify and detect type of metal object. For example, traffic lightpoles 6006. They are very large, and heavy metal objects, with aspecific shape and dimensions (about 10 meters high and an arm of about10 meters in 90 degrees to the pole).

The return signal of such a traffic light structure 6006 is very strong,and clear. Millions of such Radar images and unique signatures of suchtraffic signals may be seen, and an Artificial Intelligence programutilizing Machine Learning may be utilized to detect unique signaturesof such structures. Hence, the Radar 3702 and the Signal Processing ofthe Radar 3702, may be able to detect this type of structure byclassifying it as the specific traffic light structure 6006.

Hence, when the Radar 3702 receiver detects and classifies this type ofstructures 6006, it may determine exact direction and distance of thisstructure from the vehicle 3800. It may measure time to transmit/receivesignals and based on this may determine distance of the object. RADAR3702 may also know exact direction/azimuth to the structure 6006.

Now, since the exact location of each structure 6006 on the map iseasily determinable, it is further easy to calculate exact location ofthe vehicle 3702 with respect to the structure 6006. In this case, thedistance may be measured in centimeters which may be more accurate thandistance of GPS.

In the same manner other metal containing objects like buildings 6004,pavements 6008, road signs 6010 may also be determined and mapped withinroad traveled.

A new type of map can be generated ahead of time. This map includes theexact location of each of these objects and the specific radio-magneticsignature for this object. In many cases, big metal objects will havestrong return signature that can be detected from a very large distance.It is possible to navigate and know exact location of the vehicle 3800for autonomous driving based on exact locations of various objects onthe road. The exact location of the vehicle 3800 can be determined byazimuth and distance of the vehicle 3800 from each of these objects.

It is also possible to know exact location of the vehicle 3800 justbased on one object. It is possible to look at many objects in differentdirections (including backward, forward, right and left), differentdistances including very long distance. Based on each one of them, it ispossible to know exact location. Based on multiple objects, even betterlocation may be determined.

Furthermore, RADAR 3702, with the electromagnetic radio frequency mayalso be able to see through vegetation. That is, if a metal structure isbehind trees, vegetation or a curve, the Radar 3702 still may “see” themetal structure. Drivers' eyes visualization, Cameras, and LiDAR may notbe able to “see” the metal object if it is behind a tree.

Hence with RADAR 3702 including the ultra-low phase-noise frequencysynthesizer 3704, we are able to add classification to type of objectsidentified and determined. This may be achieved by using ArtificialIntelligence, or Machine learning, Deep Learning, and Neural Networkstechniques. This is because RADAR 3702 is able to add spectral clarityto objects within the road that may provide a new unique signature tometal within the objects.

After identification of the objects within vicinity of the vehicle 3800,a RADAR map is generated that may be stored within the database 3712 ofthe detection system 3700. This map may also be transmitted to a centralserver 6002 for further usage by other vehicles and may also be improvedupon by data collected for similar area by other vehicles as well.

FIG. 61 illustrates a block diagram of a detection system 6100 for RADARmaps generation, in accordance with an embodiment of the invention. Thesystem 6100 is similar to the detection system 3700 as discussed inconjunction with FIG. 37.

The system 6100 includes, but not limited to at least one sensor or theRADAR unit 3702, as has been described above in conjunction with FIG.39, the ultra-low phase-noise frequency synthesizer 3704, a processor6104, and a database 6106.

The RADAR unit 3702 may be configured for detecting the presence of oneor more objects/targets in one or more directions by transmitting radiosignals in the one or more directions. In an embodiment, the RADAR unit3702 may be comprised of at least one of or combination of: atraditional RADAR system, a synthetic aperture RADAR, or different typesof RADAR with different RADAR subsystems and antennas.

The ultra-low phase-noise frequency synthesizer 3704 (hereinafter mayinterchangeably be referred to as ‘frequency synthesizer 3704’) may beproviding the needed carrier frequency, or a multiple or a fraction ofthe needed carrier (or up-conversion) frequency for the RADARtransmitter section. The ultra-low noise frequency synthesizer 3704 mayalso be providing the needed carrier (or down-conversion) frequency, ora multiple or a fraction of the needed carrier frequency for the RADARreceiver section. For some RADARs the transmit signal is used as thecarrier frequency for the receive section, this does not alter theprinciple of this invention and is merely another form ofimplementation. The term “carrier” may be used interchangeably referringto an up-conversion or down-conversion signal.

The database 6106 may include instructions set having one or morespecialized instructions. The specialized instructions may be executableby the processor 6104.

In an embodiment, the RADAR unit 3702 of the system 6100 may be placedin-front of the vehicle 3800, or anywhere else in or on the vehicle3800, to transmit the radio signals in various directions and further toreceive the returned radio signals as returned from the object(s) (afterhitting the object(s)).

Further, the specialized instruction on execution by the processor 6104gathers information corresponding to the surroundings, the informationis gathered based on the returned radio signal. The information gatheredmay include, but is not limited to, surrounding information of theobject(s) such as road structure, traffic in front of those objects,traffic behavior, the response of the one or more objects based on thetraffic behavior. Further, based on analysis of these, the system 6100may obtain some specific signature of each and every object within thesurrounding of the vehicle 3800. As described earlier, each and everyobject may have different electromagnetic information and signature. Theobjects identified then may be mapped relative to the vehicle 3800. Theprocessor 6104 calculates distance, form, and type of object and addsthe information for creation of an electromagnetic map of thesurroundings of the vehicle 3800.

Further this electromagnetic map, including the unique signatures of theobjects identified by the ultra-low phase-noise frequency synthesizer3704, may be combined together with other visual maps like LiDAR maps,physical maps etc. to provide a spectral clarity. Classification of theidentified object, using the unique signatures, may be performed usingvarious Artificial Intelligence or data science techniques.

FIG. 62 illustrates a block diagram of the processor 6104 and itsvarious internal components to perform detection of objects, accordingto an embodiment of the invention. The processor 6104 includes (but notlimited to) various modules as listed. The processor 6104 may include adata acquisition module 6202, an object signature module 6204, a sensorcontrol module 6206, a classification module 6208 and a map module 6210.

The components of the processor 6104 may be hardware based. Theprocessor 6104 may include multiple microprocessors (as components) toperform different functions that may be coordinated with each other toperform a coordinated output. The processor type can be a CPU, GPU, APU,FPGA, DSP and other types of processing power technologies. Differenttype of signal processing architecture and design can be implemented inpreforming such computation.

In another aspect of the present subject matter, the modules may bemachine-readable instructions which, when executed by aprocessor/processing module, perform any of the describedfunctionalities. The machine-readable instructions may be stored on anelectronic memory device, hard disk, optical disk or othermachine-readable storage medium or non-transitory medium. In animplementation, the machine-readable instructions can also be downloadedto the storage medium via a network connection.

The data acquisition module 6202 may be configured to accept data fromthe first sensor 3702. The data acquisition module 6202 may translatethe data from the at least first sensor 3702 to a format readable to theother modules of the processor 6104.

The sensor control module 6206 may be configured to control initiation,speed and data acquisition from the at least first sensor 3702.

The surrounding environment data from the at least first sensor or theRADAR unit 3702 is received by the data acquisition module 6202 andforwarded to the object signature module 6204. Object signature module6204 obtains unique signatures of different objects from the datareceived. Further, the classification module 6206 classifies the objectsidentified using unique signatures. The classification may be based onvarious AI and data science techniques.

The map module 6210, correlates all objects identified and classifiedand generates an electro-magnetic map for the objects within thesurrounding information. Further, the map module may store these mapsand various similar maps of other surroundings within a memory. The mapsof various surroundings may be further combined to create a master map.

In an embodiment, electromagnetic maps of various surroundings may alsobe combined together, by the map module 6210, with physical maps, visualmaps, or LiDAR maps to provide more granular information to the vehicle3800 for better maneuverability while in autonomous mode.

In another embodiment, the master map may be updated real time andcontinuously by information gathered from different other vehicleswithin various surrounding. The master map may be stored in a centrallocation that may be connected to multiple vehicles. The master map maybe combined with physical maps and information may be updated anddistributed instantaneously to keep autonomous drive mode safe and up todate.

Since the mapping system 6100 utilizes specific signatures for mapcreation therefore, the GPS coordinates may not be required to ascertainthe correct positioning of the vehicle 3800. As, system 6100 isconfigured to identify the buildings and whole surrounding environment,by template matching technique, therefore the system 6100 worksefficiently in areas where there is no GPS reception as well. System6100 may utilize information from many other sensors like odometers etc.to determine exact speed and direction of the vehicle based on whichaccurate positioning data is predicted that helps in accuratemaneuvering.

Referring to FIG. 63, a flow chart illustrating a method 6300 for RADARmap generation, in accordance with an embodiment of the invention. Themethod at step 6302 initiates RADAR transmission from the RADAR unit3702. At step 6304, returned RADAR signals, after collision with nearbyobjects are received and are further analyzed. At step 6306, thereceived RADAR signals are utilized to obtain electromagneticinformation about objects and near-by surroundings. At step 6308, theelectromagnetic information is further processed to obtain uniquesignatures of objects present in the surrounding environment. Based onthe unique signatures, at step 6310, each and every object is classifiedusing various AI and data science techniques.

Further, at step 6312, the unique signatures of the objects are utilizedto further generate an electromagnetic map of the surroundingenvironment. At step 6314, the generated electromagnetic map is combinedwith a physical map that is displayed to the user at step 6316.

FIG. 64 illustrates an example embodiment of a collective mapping system6400 that can be used to build a collective map of the surroundingenvironment around the vehicle 3800. Collective mapping system 6400includes multiple primary data sensors 6402, multiple secondary datasensors 6404, a processor 6406 and a memory 6408. In the illustratedembodiments, primary data sensors include the RADAR 3702, a LiDAR system64022 and a vision system 64024. RADAR 3702, LiDAR 64022 and Visionsystem 64024 each are responsible to generate respectively a radar mapdata, LiDAR map data and image map data mapping data for use byprocessor 6406.

In an embodiment of the invention, the collective mapping system 6400may also include a plurality of secondary data sensors 6404. Theplurality of secondary data sensors 6404 are provided for real timesensing of position, location, and movement of the vehicle 3800. Thesecondary data sensors 6404 may include Global position system (GPS)64042 to provide local position of the vehicle 3800, gyroscope sensors64044 to calculate yaw of the vehicle 3800, pitch and roll, speed sensor64046, and accelerometer 64048. Weather conditions sensors 138 such astemperature, humidity and barometric pressure sensors may also beincluded with platform sensors 117. The secondary data sensors 6404 maysupplement the primary data sensors 6402 for collecting mapping data andlocalization of the vehicle 3800 within the map data acquired.

FIG. 65A, illustrates a block diagram of the processor 6406 and itsinternal processing modules according to an embodiment of the invention.The processor 6406 may be a single unit or made of multiple processingunits functioning together to achieve a common objective. In anembodiment of the invention, the multiple processing units may behardware components. However, in another embodiment, the multipleprocessing units may be software modules that may be stored asinstructions stored within the memory 6408.

The processor 6406 may include multiple modules including data fetchmodule 64062, data tagging module 64064, and combination module 64066.Operatively, data fetch module 64062 is communicably connected to theplurality of primary 6402 and secondary data sensors 6404. Further, thedata fetch module 64062 may also be connected to the memory 6408 or anyother data source of a physical maps. Hence, data fetch module 64062 isconfigured to receive RADAR map information, LiDAR map information,Visual information and physical maps information. Further, the datafetch module 64062 may be further configured to receive the mapsinformation simultaneously from different sources.

The data fetch module 64062 is also communicably connected to the datatagging module 64066. The data tagging module 64064 receives datareceived and converted into a suitable readable format by the data fetchmodule 64062. That data tagging module 64064 is configured to furthertag various data and attach an identification information to each of thedata. Furthermore, the data tagging module 64064 is communicableconnected to a combination module 64066. The combination module 64066 isconfigured to combine the different maps data received details of whichwill be explained in detail below.

FIG. 65B, illustrates a block diagram of the processor 6406 and itsinternal processing modules according to another embodiment of theinvention. The processor 6406 may include all the above-mentionedmodules along with a dynamic object removal module 6408 that may beconfigured to remove dynamic objects from the maps data received fromthe plurality of primary sensors 6402.

Dynamic objects are objects that are in continuous movement within theenvironment and remain in the environment for only a very small-timeframe like a moving object like a human being or any animal or any othermoving vehicle. Such objects may be detected by, among other things,comparing pixel details of map data from successive time slots todetermine moving objects. After such identification data of such movingobjects is removed from the map data or may be highlighted within thedata as being a dynamic object.

In other embodiments, as mentioned above, the RADAR 3702, as depicted inFIG. 39 uses the transmitter 37026 that controls emitted radio signalsin order to scan the surrounding environment of the vehicle 3800 forexample and uses the receiver 37030 to receive the reflections from theenvironment around the vehicle 3800. The RADAR 3702 is configured scanenvironment around the vehicle 3702 in various directions, along azimuthangles at one or more fixed elevation angles, or in a vertical plane atone or more fixed azimuth angles. The RADAR data can be processed togenerate, for example, a two or three-dimensional point cloud of theenvironment or a radar image of the environment, which can be stored tomemory 6408. Hence with RADAR 3702 including the ultra-low phase-noisefrequency synthesizer 3704, it is possible to add classification to typeof objects identified and determined. This may be achieved by usingArtificial Intelligence, or Machine learning, Deep Learning, and NeuralNetworks techniques. This is because RADAR 3702 is able to add spectralclarity to objects within the road that may provide a new uniquesignature to objects. This information may also be utilized by thedynamic object removal module 64068 to further remove dynamic objects asthey may be classified by the RADAR 3702 using the ultra-low phase-noisefrequency synthesizer 3704.

Further, LIDAR system 64022 is configured to scan the azimuth andelevation. Generally, a LiDAR includes dual oscillating plane mirrors,polygon mirrors, a and a laser scanner. The LIDAR system 64022 utilizesa beam splitter to collect a return signal.

Furthermore, the vision system module 64024 is configured to captureimages of an environment using a charge coupled device (CCD) sensor or aComplementary Metal Oxide Semiconductor (CMOS) sensor. These sensors maybe integrated in a digital camera, thermal imaging camera, night visioncamera, or any other vision systems known in the art. The vision systemmay also include color image sensors, a multispectral imaging camera,illuminators, or any combination known in the art.

Data obtained from RADAR, LiDAR and cameras may be supplemented withdata from the plurality of secondary sensors 6404. The supplementarydata may include various information like location data, positionaldata, movement data etc.

FIG. 66, a flow chart illustrates a method 6600 performed by the system6400 for creating collective map data. At step 6602, an existingphysical map for the location in which the vehicle 3800 is currently in,identified by the GPS sensor 64042 is accessed to form the base of themap information. Such physical maps may be preexisting from previouslymapped information or maps uploaded by other similar system usingvehicles. At step 6604, image map information is obtained using camerasensors. Further at steps 6606 and 6608 LiDAR map information and RADARdata are simultaneously captured. In other embodiments, the steps 6602,6604, 6606, and 6608 may be carried out simultaneously.

Further at step 6610, based on the RADAR data objects are identified andclassified into various categories like stationary or dynamic objects,buildings or road signs or traffic lights etc. At step 6612, the dynamicobjects are removed or at least highlighted within the map information.In an embodiment of the invention, the RADAR map data, LIDAR map dataand vision data are compared to resolve mismatch and improve thecorrectness for removal of dynamic objects. In situations of datamismatch, in one embodiment, dynamic object removal module 64068compares the data for a detected dynamic object in one map data (forexample the LiDAR map data) with the corresponding location data in theother map data (like RADAR and visual map information) to confirm thatall three maps data conform, following which the dynamic objects areeliminated or highlighted in all three datasets.

Furthermore, on removal or highlighting of the dynamic object data fromthe three maps data, the data is then combined to create a map of theenvironment by the combination module 64066. The combined map data willinclude objects identified as buildings, traffic lights, lamp posts,fire hydrants etc. Each of the objects identified based on theirspecific signatures and depicted within the map provided to the vehicle3800.

FIG. 67A, a block diagram of a surrounding environment 6700 is depictedalong with various objects within the surrounding environment, inaccordance with an embodiment of the invention. The surroundingenvironment 6700 is mapped by obtaining and recording information fromthe plurality of vehicles 6702A-D (collectively referred to as 6702. Theinformation is stored within a central database 6720. In general, togenerate the database 6720 the vehicles 6702 travelling along one ormore lanes of the environment 6700 or other surfaces collect dataregarding the roadway and nearby objects like buildings of various types6704, 6706, 6708, 6710, 6712, 6714, traffic signals of different typeslike 6716A, 6716B, and various fire hydrants 6718A-C and other objectsthat are not depicted here for sake of brevity. The objects can includeinfrastructure-based stationary targets, such as poles, streetlights,vertical guardrail posts.

The vehicles 6702 may use RADAR sensor 3702 attached to the vehicles. Inaddition to this there may be other sensors utilized like visual sensorsor LiDAR sensors. The term roadway includes conventional public roads aswell as any other area where vehicles travel such as private roads anddriveways, parking lots, parking garages, tunnels, bridges, unpavedaccess roads, etc. Data from the various sensors is collected andstored, along with an indicator of time that the data was collected sothat data from various sensors may be correlated. For example, thelocation of the vehicle 6702A at the time its corresponding radar sensorreading was collected can be determined by referencing the GPS unit ofthe vehicle 6702A at that same time. Similarly, data from other sensorsystems can be correlated with corresponding radar sensor readings aswell as the location of the vehicle 6702 obtained from the correspondingGPS units. There is no explicit assumption required on the type ofobjects in the object data collected, i.e. the database may not includeany classification of the object. This is taken care of by the object'sspecific signature from the real-time data collected by RADAR 3702 andits signature stored in the database 6720.

Further, according to an embodiment, the database 6720 may be regularlyupdated by incorporating data obtained from numerous vehicles travelingthe various roadways equipped with RADAR units similar to RADAR 3702 andGPS or LiDAR systems. The RADAR data collected, stored locally alongwith accompanying GPS data, or may be transmitted from the vehicles(e.g., in real-time using wireless data communications) to the centraldatabase 6702 where it is added into the database 100. Though,measurements from a single vehicle may be relatively inaccurate, theaverage of such multiple measurements provide high detailed information.Hence, data collected by traveling the particular routes is utilize toeither generate the database 6702 and also utilized to keep the database6702 up to date, regardless of how it is initially generated.

FIG. 67B, a block diagram illustrates localization of the vehicle 3800while traversing the environment 6700. The vehicle 3800 uses stored mapdata in the database 6720 to estimate location of the vehicle 3800 usingRADAR unit that is associated with the vehicle 3800. The vehicle 3800collects data from the corresponding RADAR sensor, although additionaldata may be obtained from one or more of the other sensor systems likeGPS unit, LiDAR sensor, camera sensor etc. The data obtained by thevehicle 3800 is matched up with comparable data samples from thedatabase 6702 (FIG. 1B).

According to an embodiment of the invention, the data from the database6720 is saved and/or converted into a format that matches the dataoutput of the sensors. In this embodiment the system generates a set ofdata that is put into a format similar to the format of the raw outputof a radar sensor (or other sensors) in order to match it with the dataextracted from the database 6720.

In some embodiments, the entire database 6720 may be stored onboard thevehicle 3800 whereas in other embodiments some parts of the database6720 may be stored onboard while the remaining portion is stored at aremote location and loaded onto the vehicle's onboard system as and whenrequired or called for. When the vehicle 3800 travels, particularlycloser to the limits of the presently-loaded portion of the database100, other portions of the database 6720 covering nearby regions may beloaded to provide coverage for areas in which the vehicle 3800 maytravel to subsequently. Detailed information regarding the velocity anddirection of the vehicle 3800 may be used to anticipate where thevehicle 3800 is going to and when it may require an access to otherportions of the database 6720.

In a general example, the RADAR 3702 combined with the ultra-lowphase-noise synthesizer 3704 fitted on the vehicle 3800 sweeps the areaand gathers information about the area through which the vehicle 3800passes. The information may be gathered using chirp function of theRADAR 3702. The chirp function along with the ultra-low phase-noisesynthesizer 3704 helps to generate pseudo visual image of objectsthrough electromagnetic waves of RADAR 3702. This chirp function and theultra-low phase-noise synthesizer 3704 obtain specific signatures ofobjects surrounding the vehicle 3800. Each and every object generatesits own specific signature. Hence it provides the way to identify andmatch the type of object with a database of such objects present withinthe environment. Dynamic objects like vehicles, humans, animals etc. canbe removed from the information gathered using simply comparisonmethods.

Hence, with an updated database 6720, it is possible to safely maneuverthe vehicle 3800 within an environment like 6700. Since, the RADARsignals gather high resolution information and it may be matched withalready available database. By knowing the movement details likedirection, speed etc. of the vehicle 3800, it is easy to predict andknow the exact current position of the vehicle. Also, it is possible topredict accurately the future position of the vehicle 3800, to even veryshort intervals. Hence, if a vehicle is moving in a certain directionand gathering information of nearby objects, that are beingsimultaneously matched to existing data of objects, if there is anyanomaly of the location it can be corrected and updated in real time.For example, in a case a vehicle is travelling a road in which abuilding is about to start in 10 mins, based on the current positionthat is based on speed of the vehicle, is noted to have been started afew minutes of the expected building location of the vehicle is updatedreal time. Also, such an information is useful at places wherein no GPSis available. Hence, the electromagnetic maps or RADAR maps generated bythe RADAR 3702 may also function as GPS at places where GPS reception isnot available.

The information gathered by the RADAR 3702 for mapping information is apseudo visual information. This means that is equivalent of a visualimage. This is possible due to high resolution data capturing of theRADAR 3702. The electromagnetic waves transmitted from the transmitter37026 are varied to perform chirp function that allows the obtaining ofspecific signatures of the objects. Basically, electromagnetic waves areutilized to calculate and generate a mathematical model equivalent ofeach object within the surrounding and matched with the database of suchmathematical models, of various objects. Hence, after a match, theprocessor 6406 would know that there is a building or a traffic signal.Further, since the database already knows the location of the vehicle,hence allowing handling of the vehicle 3800 is possible within theenvironment.

FIG. 68, illustrates a flow chart depicting a method 6800 for generationof navigational maps, in accordance with an embodiment of the invention.The RADAR 3702 is initiated at step 6802 to start transmitting RADARsignals. Further, at step 6804, using other sensors within the vehicle,like odometer etc. speed and orientation of the vehicle 3800 iscontinuously monitored. Simultaneously, at step 6806, point cloud datafor the surroundings of the vehicle 3800 is obtained.

Further, at step 6808, specific signatures of the objects within thesurrounding environment. The specific signatures of the objects help thesystem 6100 to identify the type of objects. The objects are classifiedinto stationary or dynamic objects. At step 6810, the dynamic objectsare removed from the data set obtained at previous step.

At step 6812, the RADAR data may be supplemented with a visual data thatmay be either data captured using camera sensors or LiDAR sensors.Utilizing this visual data, the point cloud data, that identifiesextent, size etc. of stationary objects is further utilized to generatea point cloud visualization data.

FIG. 69 depicts one example of many possible FMWC waveforms. Line 6902Arepresents the transmit signal as its frequency increases, and Line6904B represents the transmit signal as its frequency decreases. Line6902B and 6904B show the corresponding receive signals that are echoedback to the Radar receiver from an object. phase-noise. The double-sidedarrows 6910 and 6912 represent the frequency difference between thetransmitted and received signal, which defines the distance of thatobject. This frequency difference is also called f_(beat). FIG. 69 showsa simplistic graphic representation of calculating the distance of anobject with any FMCW signal without the presence of phase-noise. Thisresults in a very deterministic frequency difference 6910 and 6912.

FIG. 70 depicts another example of many possible FMWC waveforms, but inthis instance with phase-noise. Line 7002A represents the transmitsignal as its frequency increases, and Line 7004B represents thetransmit signal as its frequency decreases.

Line 7002B and 7004B show the corresponding receive signals that areechoed back to the Radar receiver from an object. The double-sidedarrows 7010 and 7012 again represent the frequency difference betweenthe transmitted and received signal, which defines the distance of thatobject. This is the frequency difference that is also called f_(beat).FIG. 70 shows that in the presence of Phase Noise the frequency f_(beat)is much more ambiguous and it is harder to obtain a clear measurement ofthis frequency and thus it is also impossible to achieve a deterministicvalue for the distance of the echoing object.

In an embodiment of this invention an ultra-low phase-noise synthesizermay be used to achieve a more f_(beat) and with it a more accuratedistance and range measurement for one more echoing objects.

FIG. 71 depicts a common scenario where ghost objects are created as anartifact of the Radar system and the echoing from multiple objects. Thereal objects are described as the graph crossing in circles 7106 and7108. The ghost objects are described in the graph crossings in squares7106A and 7108A.

The scenario of ghost objects is commonly mitigated by changing theslope or the entire shape of the FMCW chirp and then comparing 2 or moreFMCW shapes to determine the location of the real object.

FIG. 72 depicts the same common scenario where ghost objects are createdas an artifact of the Radar system and the echoing from multiple objectsin the presence of phase-noise. The real objects are described as thegraph crossing in circles 7206 and 7208. The ghost objects are describedin the graph crossings in squares 7206A and 7208A.

In the presence of phase-noise, and since phase-noise is a statisticalphenomenon, the real objects and the ghost objects do not appear at onespecific graph crossing, but rather in an area that is shown in thegraph as 7206, 7208, 7206A and 7208A. This area may be created byoverlaying multiple chirps.

In an embodiment of this invention the ultra-low phase-noise synthesizermay be used to determine the location of real objects faster, simplerand more accurately.

FIG. 73 shows the transformation of the frequency difference between atransmitted and received signal with the help of the Fast FourierTransform (FFT). For simplicity only 7306 is transferred to look as7322. The single frequency 7322 is uniquely relative to a specific rangeof the echoing object. In many cases the beat frequency f_(beat) ismeasured multiple times during the chirp, however, in the absence ofphase-noise this beat frequency will be exactly the same for everymeasurement resulting in a deterministic frequency representation 7322that then can be easily calculated into range.

FIG. 74 shows the same scenario as FIG. 73 but in the presence ofphase-noise. When phase-noise is present, multiple measurements off_(beat) 7406 along the chirp will create an FFT representation of manydifferent f_(beat) frequencies that are clustered together as shown in7422. When translating this into distance, a range possible distances ofthe echoing object will be obtained which is a disadvantage in terms ofthe accuracy of the entire Radar system.

In an embodiment of this invention the ultra-low phase-noise synthesizerwill cause the cluster 7422 to be very narrow so that it resembles 7322.

In another embodiment (FIG. 75) the invention of an ultra-lowphase-noise synthesizer may be used to detect stationary objects byadding phase-noise for a part of the time and then use the improvedphase-noise option for another part of the time. This method willprovide additional information that supports the detection of stationaryobject vs. moving objects.

At step 7502 a signal with bad phase-noise will be transmitted and theinaccuracy of the object location (jittering of the object over multipleparameters) will be registered in step 7504. One of these parameters maybe the doppler frequency of the object. In step 7506 a signal withultra-low phase-noise will be transmitted and in step 7508 the data fromthis transmission will be collected. Step 7510 compares the dataobtained from steps 7502 and 7506. In the case where the dopplerfrequency is 0 Hz or sufficiently close to 0 Hz and no jittering of theobject could be registered for the 2^(nd) transmit, the echoing objectis stationary and the decision-making process of a vehicle, for example,could decide to stop the vehicle or start an avoidance maneuversequence.

FIG. 76 shows a common analysis of Radar signals on 2 dimensional FFTgrid. One dimension is the Range (or distance) of the echoing object andthe other is the velocity of the echoing object. The darker unit squarethe more energy is echoed back from that spot.

With traditional Radars an object at a certain range with a certainvelocity manifests itself with very high energy in a few neighboringunit squares as can be seen at 7602A and 7604A. Since the energy isspread over several unit squares, it is very difficult for adecision-making system to determine the distance end velocity of anobject. In addition, many modern Radar systems use multiple transmit andreceive paths mainly to determine the direction of arrival of theechoing signal which multiplies the complexity of the 2-dimensional FFTgrid.

In an embodiment of this invention a Radar system utilizing an ultra-lowphase-noise synthesizer will create much smaller areas of energy on the2-dimensional FFT grid because the energy of the echoed back from andobject will be much more focused and with a higher amplitude at in thatarea.

7602B and 7604B show the echo of the same targeted objects as in 7602Aand 7602 as it would be created by a Radar System utilizing an ultra-lowphase-noise synthesizer. The energy is concentrated in fewer gridsquares which means that the location and velocity of the object can bedetermined much more accurately. Relating this principal to MultipleInput Multiple Output (MIMO) Radar Systems for each transmit and receivepath pair such a matrix may be created. A multiple array of2-dimensional FFT matrices may be used to determine the angle of arrivalof multiple echoing signals returning from the same object. Since thematrices created by a Radar system utilizing an ultra-low phase-noisesynthesizer is very accurate in terms of determining the distance andvelocity of the object this set of matrices may be used to determine theangle of arrival of these echoes accurately and this determine the exactlocation and velocity of the object.

The methods above are in addition to the improved clutter to Signal orSignal to Interference and Noise ratio (SINR).

FIG. 77 illustrates an example embodiment of a long range, ample timeadvanced notice detection and classification system. The system providesdata about the potential and probability of the existence of an objectat a long-range distance. This data may be fused with other sensors forearly processing and focusing to the area and direction of the suspectedobject. The system 7700 may be used as a long-range detection system ofthe surrounding environment around the vehicle 3800. The long-rangeradar detection system 7700 includes multiple primary data sensors 7702,multiple secondary data sensors 7704, a processor 7706 and a memory7708. In the illustrated embodiments, primary data sensors include theRADAR 3702, a LiDAR system 77022 and a vision system 77024.

RADAR 3702 is responsible to provide long-range early detection data.LiDAR 77022, Vision system 77024, Sensor 7704, Processor 7706 and Memory7708 are responsible for processing the long-range early detection data.The processor 7706 may poll different sensors to gather more data aspart of the detection and classification process needed for perception.The polling mechanism may rely on processing 7706 or memory 7708 orboth. The polling by processor 7706 may also instruct sensors 7702 and7704 to focus on specific areas at which an object was detected by RADAR3702.

Processor 7706 and Memory 7708 may be on a central computation system inthe autonomous vehicle or outside of it. The processor 7706 and Memory7708 may also be embedded as part of the sensor set 7702 or 7704 toallow a direct closed loop computation among multiple sensors and allowlong-range early detection. The processing power of 7702 and 7704 mayexist in one of the sensors or distributed among them.

The processor 7706 and Memory 7708 may also take a form of distributedprocessing relying on processing power available in sensors, lowutilized processors in the vehicle and outside of it.

While the invention has been described in detail, modifications withinthe spirit and scope of the invention will be readily apparent to thoseof skill in the art. Such modifications are also to be considered aspart of the present disclosure. In view of the foregoing discussion,relevant knowledge in the art and references or information discussedabove in connection with the Background, which are all incorporatedherein by reference, further description is deemed unnecessary. Inaddition, it should be understood that aspects of the invention andportions of various embodiments may be combined or interchanged eitherin whole or in part. Furthermore, those of ordinary skill in the artwill appreciate that the foregoing description is by way of example onlyand is not intended to limit the invention.

The foregoing discussion of the present disclosure has been presentedfor purposes of illustration and description. It is not intended tolimit the present disclosure to the form or forms disclosed herein. Inthe foregoing Detailed Description, for example, various features of thepresent disclosure are grouped together in one or more embodiments,configurations, or aspects for the purpose of streamlining thedisclosure. The features of the embodiments, configurations, or aspectsmay be combined in alternate embodiments, configurations, or aspectsother than those discussed above. This method of disclosure is not to beinterpreted as reflecting an intention the present disclosure requiresmore features than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive aspects lie in less than allfeatures of a single foregoing disclosed embodiment, configuration, oraspect. Thus, the following claims are hereby incorporated into thisDetailed Description, with each claim standing on its own as a separateembodiment of the present disclosure.

Moreover, though the description of the present disclosure has includeddescription of one or more embodiments, configurations, or aspects andcertain variations and modifications, other variations, combinations,and modifications are within the scope of the present disclosure, e.g.,as may be within the skill and knowledge of those in the art, afterunderstanding the present disclosure. It is intended to obtain rightswhich include alternative embodiments, configurations, or aspects to theextent permitted, including alternate, interchangeable and/or equivalentstructures, functions, ranges or steps to those claimed, whether or notsuch alternate, interchangeable and/or equivalent structures, functions,ranges or steps are disclosed herein, and without intending to publiclydedicate a patentable subject matter.

We claim:
 1. A system for detecting the surrounding environment of avehicle comprising: at least first sensor, configured to obtain data,the at least first sensor comprising: A) a transmitter for transmittingat least one radio signal to the one or more objects within thesurrounding environment; B) a receiver for receiving the at least oneradio signal returned from the one or more objects; and C) at least oneultra-low phase noise frequency synthesizer configured to determinephase noise and quality of the transmitted and the received at least oneradio signal, wherein the at least one ultra-low phase noise frequencysynthesizer comprises at least one sampling Phase Locked Loop (PLL) andat least one main PLL, said Sampling PLL comprises a sampling phasedetector and said main PLL comprises a high frequency digitalphase/frequency detector; and a processing unit coupled to the at leastfirst sensor configured to; a) gather, electro-magnetic informationabout the one or more objects; b) classify or recognize each of the oneor more objects by analyzing the data, wherein the classification orrecognition is based on a unique signature obtained from each of the oneor more objects; c) generate an electromagnetic map of the surroundingenvironment by utilizing unique signatures of the one or more objects;and d) combine the electromagnetic map with a geographical map orphysical map.
 2. The system of claim 1, wherein the at least firstsensor is a RADAR sensor.
 3. The system of claim 1, wherein the dataincludes information about the electromagnetic properties andcharacteristics about the object of interest or the surroundings of thevehicle.
 4. The system of claim 1, wherein the data includes shape,silhouette, doppler or micro doppler information.
 5. The system of claim1, wherein the data includes depth, dimensions, direction, height,distance and placement of the object of interest with respect to thevehicle.
 6. The system of claim 1, wherein the classification type ofthe one or more objects includes living or non-living thing, stationaryor moving object, animal or human, standing or mobile human, metal,wood, or concrete.
 7. A system for detecting the surrounding environmentof a vehicle comprising: at least first sensor, configured to obtaindata, the at least first sensor comprising; A) a transmitter fortransmitting at least one radio signal to the one or more objects withinthe surrounding environment; B) a receiver for receiving the at leastone radio signal returned from the one or more objects; and C) at leastone ultra-low phase noise frequency synthesizer configured to determinephase noise and quality of the transmitted and the received at least oneradio signal, wherein the at least one ultra-low phase noise frequencysynthesizer comprises at least one sampling Phase Locked Loop (PLL) andat least one main PLL, said Sampling PLL comprises a sampling phasedetector and said main PLL comprises a high frequency digitalphase/frequency detector; and a processing unit coupled to the at leastfirst sensor configured to; a) gather, electro-magnetic informationabout the one or more objects; b) classify or recognize each of the oneor more objects by analyzing the data, wherein the classification orrecognition is based on a unique signature obtained from each of the oneor more objects; c) generate an electromagnetic map of the surroundingenvironment by utilizing unique signatures of the one or more objects;and d) combine the electromagnetic map with a geographical map orphysical map.
 8. The system of claim 7, wherein the sensor is a RADARsensor.
 9. The system of claim 7, wherein the data includes informationabout the electromagnetic properties and characteristics about theobject of interest or the surroundings of the vehicle.
 10. The system ofclaim 7, wherein the data includes shape, silhouette, doppler or microdoppler information.
 11. The system of claim 7, wherein the dataincludes depth, dimensions, direction, height, distance and placement ofthe object of interest with respect to the vehicle.
 12. The system ofclaim 7, wherein the classification type of the object of interestincludes living or non-living thing, stationary or moving object, animalor human, standing or mobile human, metal or wood.
 13. A system fordetecting the surrounding environment of a vehicle comprising: at leastfirst sensor, configured to obtain data, the at least first sensorcomprising A) a transmitter for transmitting at least one radio signalto the one or more objects within the surrounding environment; B) areceiver for receiving the at least one radio signal returned from theone or more objects; and C) at least one ultra-low phase noise frequencysynthesizer configured to determine phase noise and quality of thetransmitted and the received at least one radio signal, wherein the atleast one ultra-low phase noise frequency synthesizer comprises at leastone sampling Phase Locked Loop (PLL) and at least one main PLL, saidSampling PLL comprises a sampling phase detector and said main PLLcomprises a high frequency digital phase/frequency detector; and aprocessing unit coupled to the at least first sensor configured to; a)gather, electro-magnetic information about the one or more objects; b)classify or recognize each of the one or more objects by analyzing thedata, wherein the classification or recognition is based on a uniquesignature obtained from each of the one or more objects; c) generate anelectromagnetic map of the surrounding environment by utilizing uniquesignatures of the one or more objects; and d) combine theelectromagnetic map with a geographical map or physical map.
 14. Thesystem of claim 13, wherein the sensor is a RADAR sensor.
 15. The systemof claim 13, wherein the data includes information about theelectromagnetic properties and characteristics about the object ofinterest or the surroundings of the vehicle.
 16. The system of claim 13,wherein the data includes shape, silhouette, doppler or micro dopplerinformation.
 17. The system of claim 13, wherein the data includesdepth, dimensions, direction, height, distance and placement of theobject of interest with respect to the vehicle.
 18. The system of claim13, wherein the classification type of the object of interest includesliving or non-living thing, stationary or moving object, animal orhuman, standing or mobile human, metal, wood, or concrete.
 19. A methodfor detecting the surrounding environment of a vehicle comprising: A.utilizing at least a first sensor to obtain a data, the at least firstsensor being utilized for: i. transmitting, by a transmitter, at leastone radio signal to the one or more objects within the surroundingenvironment; ii. receiving, by a receiver, the at least one radio signalreturned from the one or more objects; and iii. determining phase noiseand quality of the transmitted and the received at least one radiosignal by at least one ultra-low phase noise frequency synthesizer,wherein the at least one ultra-low phase noise frequency synthesizercomprises at least one sampling Phase Locked Loop (PLL) and at least onemain PLL, said Sampling PLL comprises a sampling phase detector and saidmain PLL comprises a high frequency digital phase/frequency detector;and B. gathering, electro-magnetic information about the one or moreobjects; C. classifying or recognize each of the one or more objects byanalyzing the data, wherein the classification or recognition is basedon a unique signature obtained from each of the one or more objects; D.generating an electromagnetic map of the surrounding environment byutilizing unique signatures of the one or more objects; and E. combiningthe electromagnetic map with a geographical map or physical map.
 20. Themethod of claim 19, further comprising recognizing the object oninterest after classifying, wherein the classification of the object ofinterest includes living or non-living thing, stationary or movingobject, animal or human, standing or mobile human, metallic, wooden, orconcrete objects.